{ "case": 7, "report_id": "d73396cc-4ad6-493e-a69a-0cd2a0a435e6", "question": "同一次分箱分析怎样交付给不同使用者？", "consumer": "deterministic public API queries; no LLM", "description": { "report_type": "MarsBinningReport", "report_id": "d73396cc-4ad6-493e-a69a-0cd2a0a435e6", "format_version": 1, "feature_metadata": { "NAME_CONTRACT_TYPE": { "display_name": "贷款合同类型", "description": "Identification if loan is cash or revolving", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "CODE_GENDER": { "display_name": "客户性别", "description": "Gender of the client", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_OWN_CAR": { "display_name": "是否拥有汽车", "description": "Flag if the client owns a car", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_OWN_REALTY": { "display_name": "是否拥有住宅", "description": "Flag if client owns a house or flat", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "CNT_CHILDREN": { "display_name": "子女人数", "description": "Number of children the client has", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_INCOME_TOTAL": { "display_name": "客户收入", "description": "Income of the client", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "原始金额单位（币种未公开）" }, "AMT_ANNUITY": { "display_name": "贷款年金", "description": "Loan annuity", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "原始金额单位（币种未公开）" }, "AMT_GOODS_PRICE": { "display_name": "贷款对应商品价格", "description": "For consumer loans it is the price of the goods for which the loan is given", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "原始金额单位（币种未公开）" }, "NAME_TYPE_SUITE": { "display_name": "申请时陪同人员类型", "description": "Who was accompanying client when he was applying for the loan", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "NAME_INCOME_TYPE": { "display_name": "收入类型", "description": "Clients income type (businessman, working, maternity leave,…)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "NAME_EDUCATION_TYPE": { "display_name": "最高教育程度", "description": "Level of highest education the client achieved", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "NAME_FAMILY_STATUS": { "display_name": "家庭状态", "description": "Family status of the client", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "NAME_HOUSING_TYPE": { "display_name": "居住情况", "description": "What is the housing situation of the client (renting, living with parents, ...)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REGION_POPULATION_RELATIVE": { "display_name": "居住地区归一化人口规模", "description": "Normalized population of region where client lives (higher number means the client lives in more populated region)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "DAYS_BIRTH": { "display_name": "申请时年龄天数（原始值）", "description": "Client's age in days at the time of application", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "天（相对申请日，保留原始符号）" }, "DAYS_EMPLOYED": { "display_name": "开始当前工作相对申请的天数", "description": "How many days before the application the person started current employment", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "天（相对申请日，保留原始符号）" }, "DAYS_REGISTRATION": { "display_name": "变更登记相对申请的天数", "description": "How many days before the application did client change his registration", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "天（相对申请日，保留原始符号）" }, "DAYS_ID_PUBLISH": { "display_name": "变更身份证件相对申请的天数", "description": "How many days before the application did client change the identity document with which he applied for the loan", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "天（相对申请日，保留原始符号）" }, "OWN_CAR_AGE": { "display_name": "汽车年龄", "description": "Age of client's car", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_MOBIL": { "display_name": "是否提供手机号码", "description": "Did client provide mobile phone (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_EMP_PHONE": { "display_name": "是否提供工作电话", "description": "Did client provide work phone (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_WORK_PHONE": { "display_name": "是否提供家庭电话（官方字段说明）", "description": "Did client provide home phone (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_CONT_MOBILE": { "display_name": "手机是否可联系", "description": "Was mobile phone reachable (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_PHONE": { "display_name": "是否提供家庭电话", "description": "Did client provide home phone (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_EMAIL": { "display_name": "是否提供电子邮箱", "description": "Did client provide email (1=YES, 0=NO)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "OCCUPATION_TYPE": { "display_name": "职业类型", "description": "What kind of occupation does the client have", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "CNT_FAM_MEMBERS": { "display_name": "家庭成员人数", "description": "How many family members does client have", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "REGION_RATING_CLIENT": { "display_name": "居住地区评级", "description": "Our rating of the region where client lives (1,2,3)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REGION_RATING_CLIENT_W_CITY": { "display_name": "考虑城市的居住地区评级", "description": "Our rating of the region where client lives with taking city into account (1,2,3)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "WEEKDAY_APPR_PROCESS_START": { "display_name": "申请星期", "description": "On which day of the week did the client apply for the loan", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "HOUR_APPR_PROCESS_START": { "display_name": "申请大致小时", "description": "Approximately at what hour did the client apply for the loan", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REG_REGION_NOT_LIVE_REGION": { "display_name": "永久与联系地址地区是否不同", "description": "Flag if client's permanent address does not match contact address (1=different, 0=same, at region level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REG_REGION_NOT_WORK_REGION": { "display_name": "永久与工作地址地区是否不同", "description": "Flag if client's permanent address does not match work address (1=different, 0=same, at region level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "LIVE_REGION_NOT_WORK_REGION": { "display_name": "联系与工作地址地区是否不同", "description": "Flag if client's contact address does not match work address (1=different, 0=same, at region level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REG_CITY_NOT_LIVE_CITY": { "display_name": "永久与联系地址城市是否不同", "description": "Flag if client's permanent address does not match contact address (1=different, 0=same, at city level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "REG_CITY_NOT_WORK_CITY": { "display_name": "永久与工作地址城市是否不同", "description": "Flag if client's permanent address does not match work address (1=different, 0=same, at city level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "LIVE_CITY_NOT_WORK_CITY": { "display_name": "联系与工作地址城市是否不同", "description": "Flag if client's contact address does not match work address (1=different, 0=same, at city level)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "ORGANIZATION_TYPE": { "display_name": "工作单位类型", "description": "Type of organization where client works", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "EXT_SOURCE_1": { "display_name": "外部来源分 1", "description": "Normalized score from external data source", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "EXT_SOURCE_2": { "display_name": "外部来源分 2", "description": "Normalized score from external data source", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "EXT_SOURCE_3": { "display_name": "外部来源分 3", "description": "Normalized score from external data source", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "APARTMENTS_AVG": { "display_name": "建筑公寓信息归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "BASEMENTAREA_AVG": { "display_name": "地下室面积归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BEGINEXPLUATATION_AVG": { "display_name": "建筑开始使用年限归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BUILD_AVG": { "display_name": "建筑建成年限归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "COMMONAREA_AVG": { "display_name": "公共区域面积归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ELEVATORS_AVG": { "display_name": "电梯数量归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ENTRANCES_AVG": { "display_name": "入口数量归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMAX_AVG": { "display_name": "最高楼层归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMIN_AVG": { "display_name": "最低楼层归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LANDAREA_AVG": { "display_name": "土地面积归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAPARTMENTS_AVG": { "display_name": "居住公寓信息归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAREA_AVG": { "display_name": "居住面积归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAPARTMENTS_AVG": { "display_name": "非居住公寓信息归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAREA_AVG": { "display_name": "非居住面积归一化均值", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "APARTMENTS_MODE": { "display_name": "建筑公寓信息归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "BASEMENTAREA_MODE": { "display_name": "地下室面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BEGINEXPLUATATION_MODE": { "display_name": "建筑开始使用年限归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BUILD_MODE": { "display_name": "建筑建成年限归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "COMMONAREA_MODE": { "display_name": "公共区域面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ELEVATORS_MODE": { "display_name": "电梯数量归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ENTRANCES_MODE": { "display_name": "入口数量归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMAX_MODE": { "display_name": "最高楼层归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMIN_MODE": { "display_name": "最低楼层归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LANDAREA_MODE": { "display_name": "土地面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAPARTMENTS_MODE": { "display_name": "居住公寓信息归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAREA_MODE": { "display_name": "居住面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAPARTMENTS_MODE": { "display_name": "非居住公寓信息归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAREA_MODE": { "display_name": "非居住面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "APARTMENTS_MEDI": { "display_name": "建筑公寓信息归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "BASEMENTAREA_MEDI": { "display_name": "地下室面积归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BEGINEXPLUATATION_MEDI": { "display_name": "建筑开始使用年限归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "YEARS_BUILD_MEDI": { "display_name": "建筑建成年限归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "COMMONAREA_MEDI": { "display_name": "公共区域面积归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ELEVATORS_MEDI": { "display_name": "电梯数量归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "ENTRANCES_MEDI": { "display_name": "入口数量归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMAX_MEDI": { "display_name": "最高楼层归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FLOORSMIN_MEDI": { "display_name": "最低楼层归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LANDAREA_MEDI": { "display_name": "土地面积归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAPARTMENTS_MEDI": { "display_name": "居住公寓信息归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "LIVINGAREA_MEDI": { "display_name": "居住面积归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAPARTMENTS_MEDI": { "display_name": "非居住公寓信息归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "NONLIVINGAREA_MEDI": { "display_name": "非居住面积归一化中位数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "FONDKAPREMONT_MODE": { "display_name": "建筑信息 FONDKAPREMONT 众数（细项未公开）", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "HOUSETYPE_MODE": { "display_name": "住房类型众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "TOTALAREA_MODE": { "display_name": "建筑总面积归一化众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "WALLSMATERIAL_MODE": { "display_name": "墙体材料众数", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "EMERGENCYSTATE_MODE": { "display_name": "建筑状态信息众数（细项未公开）", "description": "Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "归一化值（无量纲）" }, "OBS_30_CNT_SOCIAL_CIRCLE": { "display_name": "社交圈可观察的 30 天逾期记录数", "description": "How many observation of client's social surroundings with observable 30 DPD (days past due) default", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "DEF_30_CNT_SOCIAL_CIRCLE": { "display_name": "社交圈发生 30 天逾期的记录数", "description": "How many observation of client's social surroundings defaulted on 30 DPD (days past due)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "OBS_60_CNT_SOCIAL_CIRCLE": { "display_name": "社交圈可观察的 60 天逾期记录数", "description": "How many observation of client's social surroundings with observable 60 DPD (days past due) default", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "DEF_60_CNT_SOCIAL_CIRCLE": { "display_name": "社交圈发生 60 天逾期的记录数", "description": "How many observation of client's social surroundings defaulted on 60 (days past due) DPD", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "DAYS_LAST_PHONE_CHANGE": { "display_name": "更换电话相对申请的天数", "description": "How many days before application did client change phone", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "天（相对申请日，保留原始符号）" }, "FLAG_DOCUMENT_2": { "display_name": "是否提供文件 2", "description": "Did client provide document 2", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_3": { "display_name": "是否提供文件 3", "description": "Did client provide document 3", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_4": { "display_name": "是否提供文件 4", "description": "Did client provide document 4", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_5": { "display_name": "是否提供文件 5", "description": "Did client provide document 5", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_6": { "display_name": "是否提供文件 6", "description": "Did client provide document 6", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_7": { "display_name": "是否提供文件 7", "description": "Did client provide document 7", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_8": { "display_name": "是否提供文件 8", "description": "Did client provide document 8", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_9": { "display_name": "是否提供文件 9", "description": "Did client provide document 9", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_10": { "display_name": "是否提供文件 10", "description": "Did client provide document 10", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_11": { "display_name": "是否提供文件 11", "description": "Did client provide document 11", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_12": { "display_name": "是否提供文件 12", "description": "Did client provide document 12", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_13": { "display_name": "是否提供文件 13", "description": "Did client provide document 13", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_14": { "display_name": "是否提供文件 14", "description": "Did client provide document 14", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_15": { "display_name": "是否提供文件 15", "description": "Did client provide document 15", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_16": { "display_name": "是否提供文件 16", "description": "Did client provide document 16", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_17": { "display_name": "是否提供文件 17", "description": "Did client provide document 17", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_18": { "display_name": "是否提供文件 18", "description": "Did client provide document 18", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_19": { "display_name": "是否提供文件 19", "description": "Did client provide document 19", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_20": { "display_name": "是否提供文件 20", "description": "Did client provide document 20", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "FLAG_DOCUMENT_21": { "display_name": "是否提供文件 21", "description": "Did client provide document 21", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "未公开" }, "AMT_REQ_CREDIT_BUREAU_HOUR": { "display_name": "申请前一小时征信查询次数", "description": "Number of enquiries to Credit Bureau about the client one hour before application", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_REQ_CREDIT_BUREAU_DAY": { "display_name": "申请前一天征信查询次数（不含前一小时）", "description": "Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_REQ_CREDIT_BUREAU_WEEK": { "display_name": "申请前一周征信查询次数（不含前一天）", "description": "Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_REQ_CREDIT_BUREAU_MON": { "display_name": "申请前一个月征信查询次数（不含前一周）", "description": "Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_REQ_CREDIT_BUREAU_QRT": { "display_name": "申请前三个月征信查询次数（不含前一个月）", "description": "Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" }, "AMT_REQ_CREDIT_BUREAU_YEAR": { "display_name": "申请前一年征信查询次数（官方说明原文有歧义）", "description": "Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application)", "data_source": "Home Credit 2018 application_{train|test}.csv", "unit": "次数或人数（按官方定义）" } }, "source": { "kind": "analysis", "producer": "mars", "input_provenance": "caller_provided_dataframe" }, "context_source": { "business_context": { "label_definition": "unknown", "observation_window": "unknown", "currency": "unknown", "dataset_id": "user_provided", "source": "user_provided", "real_features": "user_provided", "target": "user_provided", "date": "user_provided", "partitions": "user_provided", "amount": "user_provided", "weights": "user_provided", "sample_scope": "user_provided", "preparation_version": "user_provided", "special_values": "user_provided", "labels": "user_provided" }, "parameters": "calculation" }, "serialization": { "non_finite": "tagged float: {$mars: float, value: nan/inf/-inf}", "date": "ISO-8601", "null": "missing; calculation state is separate" }, "tables": { "summary": { "rows": 119, "index": null, "grain": "feature", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "iv": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "ks": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "auc": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "psi_max": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数；跨有效分组最大值" }, "rc_min": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数；跨有效分组最小值" }, "lift_min": { "dtype": "Float64", "unit": "dimensionless", "meaning": "Total 正常箱 Lift 的最小值" }, "lift_max": { "dtype": "Float64", "unit": "dimensionless", "meaning": "Total 正常箱 Lift 的最大值" }, "missing": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "missing_min": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值；跨有效分组最小值" }, "missing_max": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值；跨有效分组最大值" }, "mono": { "dtype": "Float64", "unit": "correlation", "meaning": "Total 正常箱索引与坏率的 Spearman 相关系数；单箱或坏率恒定时无法定义" } } }, "detail": { "rows": 3257, "index": null, "grain": "target/feature/group/bin", "fields": { "y": { "dtype": "String", "unit": "identifier", "meaning": "目标列标识；真实目标及无标签状态见参数" }, "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "trend": { "dtype": "String", "unit": "category", "meaning": "Total 正常箱 WOE 形态；无标签时未计算" }, "mars_group": { "dtype": "String", "unit": "dimension", "meaning": "实际计算分组字段；取值保留原始分组标识" }, "bin_index": { "dtype": "Int16", "unit": "index", "meaning": "分箱规则索引；缺失和特殊箱为负，汇总行由 bin_type 标识" }, "bin_label": { "dtype": "String", "unit": "category", "meaning": "来自拟合分箱规则的箱标签" }, "count": { "dtype": "Float64", "unit": "count", "meaning": "全样本数；配置 weights_col 时为样本权重和" }, "observed_count": { "dtype": "Float64", "unit": "count", "meaning": "已表现标签样本数或权重和；无标签时未计算" }, "bad": { "dtype": "Float64", "unit": "count", "meaning": "已表现坏样本数或权重和" }, "good": { "dtype": "Float64", "unit": "count", "meaning": "observed_count-bad" }, "pct": { "dtype": "Float64", "unit": "ratio", "meaning": "箱样本量/分组全样本量，配置权重时为权重和之比" }, "bad_rate": { "dtype": "Float64", "unit": "ratio", "meaning": "坏人数/有表现人数；加权时为坏权重/有表现权重，见 parameters" }, "lift": { "dtype": "Float64", "unit": "dimensionless", "meaning": "箱坏率/总体坏率，复用稳定化规则" }, "cum_count": { "dtype": "Float64", "unit": "count", "meaning": "按分箱索引展示顺序的累计全样本量或权重和" }, "cum_observed_count": { "dtype": "Float64", "unit": "count", "meaning": "按分箱索引展示顺序的累计已表现样本量或权重和" }, "cum_bad": { "dtype": "Float64", "unit": "count", "meaning": "按分箱索引展示顺序的累计坏样本量或权重和" }, "cum_bad_rate": { "dtype": "Float64", "unit": "ratio", "meaning": "累计 bad/累计 observed_count" }, "psi_bin": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前箱 PSI 贡献；缺失箱和特殊箱由参数控制" }, "ks_bin": { "dtype": "Float64", "unit": "points_0_100", "meaning": "当前箱累计坏样本与好样本分布绝对差 ×100" }, "auc_bin": { "dtype": "Float64", "unit": "ratio", "meaning": "分箱 ROC 梯形面积贡献；汇总行进行 AUC 归一" }, "iv_bin": { "dtype": "Float32", "unit": "dimensionless", "meaning": "(bad_dist-good_dist)×WOE" }, "total_count": { "dtype": "Float64", "unit": "count", "meaning": "分组全样本数或权重和" }, "bin_type": { "dtype": "String", "unit": "category", "meaning": "正常、缺失、特殊或汇总箱类型" }, "tot_amt": { "dtype": "Float64", "unit": "unknown_business_unit", "meaning": "全样本金金额和；业务币种未知" }, "good_amt": { "dtype": "Float64", "unit": "unknown_business_unit", "meaning": "好样本金金额和；业务币种未知" }, "bad_amt": { "dtype": "Float64", "unit": "unknown_business_unit", "meaning": "坏样本金金额和；业务币种未知" }, "avg_amt": { "dtype": "Float64", "unit": "unknown_business_unit", "meaning": "tot_amt/count；配置样本权重时 count 为权重和；业务币种未知" }, "amt_bad_rate": { "dtype": "Float64", "unit": "ratio", "meaning": "bad_amt/observed_amt；未表现金额不计入分母，分母非正时为空" }, "lift_amt": { "dtype": "Float64", "unit": "dimensionless", "meaning": "箱金额坏率/总体金额坏率，复用稳定化规则" } } }, "trend.psi": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" }, "1val": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" }, "2test": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" }, "3oot": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" }, "4_oot_undue": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" }, "Total": { "dtype": "Float64", "unit": "dimensionless", "meaning": "各箱 (actual-expected)×ln(actual/expected) 之和；参考及箱范围见参数" } }, "target": "TARGET" }, "trend.auc": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "1val": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "2test": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "3oot": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "4_oot_undue": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" }, "Total": { "dtype": "Float64", "unit": "ratio", "meaning": "按配置排序的分箱 ROC 梯形面积；报告归一到不小于 0.5" } }, "target": "TARGET" }, "trend.ks": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "1val": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "2test": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "3oot": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "4_oot_undue": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" }, "Total": { "dtype": "Float64", "unit": "points_0_100", "meaning": "按实际配置排序的分箱累计坏样本与好样本分布最大绝对差 ×100" } }, "target": "TARGET" }, "trend.iv": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "1val": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "2test": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "3oot": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "4_oot_undue": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" }, "Total": { "dtype": "Float32", "unit": "dimensionless", "meaning": "各箱 (bad_dist-good_dist)×WOE 之和，复用稳定化规则" } }, "target": "TARGET" }, "trend.missing": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "1val": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "2test": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "3oot": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "4_oot_undue": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" }, "Total": { "dtype": "Float64", "unit": "ratio", "meaning": "缺失箱样本量/全样本量；画像包括 Null、NaN 和配置缺失值" } }, "target": "TARGET" }, "trend.lift": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" }, "1val": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" }, "2test": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" }, "3oot": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" }, "4_oot_undue": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" }, "Total": { "dtype": "Float64", "unit": "dimensionless", "meaning": "当前分组箱 Lift 的最大值；未表现分组为空" } }, "target": "TARGET" }, "trend.risk_corr": { "rows": 119, "index": null, "grain": "feature (groups in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "0train": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" }, "1val": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" }, "2test": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" }, "3oot": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" }, "4_oot_undue": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" }, "Total": { "dtype": "Float64", "unit": "correlation", "meaning": "正常箱坏率与 RC 参考坏率的 Spearman 相关系数，参考来源见参数" } }, "target": "TARGET" }, "calculation_status": { "rows": 833, "index": null, "grain": "target/feature/group/metric", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "target": { "dtype": "String", "unit": "identifier", "meaning": "真实目标列标识" }, "group": { "dtype": "String", "unit": "dimension", "meaning": "状态所属分组；Total 为全量" }, "metric": { "dtype": "String", "unit": "identifier", "meaning": "计算状态适用的指标或指标族" }, "status": { "dtype": "String", "unit": "state", "meaning": "computed 已计算（有效零仍为零）；not_computed 未计算；unobserved 无观测标签；undefined 无法定义；insufficient_samples 样本不足；failed 计算失败；skipped 跳过；结合 reason" }, "reason": { "dtype": "String", "unit": "state_reason", "meaning": "no_target 未提供目标；no_observed_labels 无已观测标签；single_observed_class 仅一种观测类别；其他失败、跳过、样本不足或无法定义原因；空值不替代有效零" } } }, "missing_by_day": { "rows": 119, "index": null, "grain": "feature (days in columns)", "fields": { "feature": { "dtype": "String", "unit": "identifier", "meaning": "原输入特征名称" }, "display_name": { "dtype": "String", "unit": "category", "meaning": "调用方校验后的特征可读名称；缺失可在展示时回退原始 ID" }, "dtype": { "dtype": "String", "unit": "type", "meaning": "原输入字段的数据类型" }, "data_source": { "dtype": "String", "unit": "category", "meaning": "调用方登记的特征来源；未映射字段可能标为 UNMAPPED" }, "2026-01-01": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-02": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-03": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-04": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-05": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-06": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-07": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-08": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-09": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-10": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-11": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-12": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-13": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-14": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-15": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-16": { "dtype": "Float64", "unit": "ratio", "meaning": "自然日原始缺失值数/当日样本数，含 Null、NaN 和配置缺失值；不使用样本权重，total 为全量比例" }, "2026-01-17": { "dtype": 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0.08072881945686496 }, "feature_start_aware_reference": false, "psi_include_missing": false, "psi_include_special": false, "risk_corr_baseline": "total", "risk_corr_reference_source": "total", "amount_col": "AMT_CREDIT", "feature_start_reference_features": [], "feature_start_reference_dates": {}, "group_context": { "input_group_col": "dataset_flag", "internal_group_col": "mars_group", "groups": [ { "internal_value": "0train", "display_value": "0train", "start_dt": "2026-01-01", "end_dt": "2026-05-07" }, { "internal_value": "1val", "display_value": "1val", "start_dt": "2026-05-08", "end_dt": "2026-05-25" }, { "internal_value": "2test", "display_value": "2test", "start_dt": "2026-05-26", "end_dt": "2026-06-12" }, { "internal_value": "3oot", "display_value": "3oot", "start_dt": "2026-06-13", "end_dt": "2026-06-30" }, { "internal_value": "4_oot_undue", "display_value": "4_oot_undue", "start_dt": "2026-07-01", "end_dt": "2026-07-31" } ], "total_range": { "start_dt": "2026-01-01", "end_dt": "2026-07-31" } } }, "business_context": { "label_definition": "unknown", "observation_window": "unknown", "currency": "unknown", "dataset_id": "home-credit-default-risk-2018", "source": "https://www.kaggle.com/competitions/home-credit-default-risk/data", "real_features": { "source": "application_train.csv/application_test.csv", "simulated": false, "derived_features_added": false }, "target": { "field": "TARGET", "definition": "Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases)", "simulated": false, "available_in": "application_train.csv", "unavailable_in": "application_test.csv", "unobserved_value": null, "performance_window": "X/Y 未公开；不推断 bad30/bad60" }, "date": { "field": "APPLY_DATE", "simulated": true, "train_range": [ "2026-01-01", "2026-06-30" ], "test_range": [ "2026-07-01", "2026-07-31" ], "limitation": "功能演示日期，不支持真实跨期泛化、季节性或线上漂移结论。" }, "partitions": { "field": "dataset_flag", "simulated": true, "0train": "拟合、候选发现、开发筛选与 PSI 参考分布", "1val": "验证或筛选；参与选择后不是最终独立评估", "2test": "有标签独立评估，不等同于原始 application_test", "3oot": "模拟时间外独立评估，不是真实业务 OOT", "4_oot_undue": "原始比赛测试表，标签未公开；名称不代表真实贷款未到期" }, "amount": { "field": "AMT_CREDIT", "unit": "原始金额单位", "currency": "未公开", "meaning": "贷款金额，不等于损失或利润", "included_as_feature": false }, "weights": null, "sample_scope": "两个 application 文件的全部样本", "preparation_version": "home-credit-2018-v1", "special_values": { "DAYS_EMPLOYED": { "value": 365243, "count": 64648, "handling": "保留原值；该异常正值作为本字段的特殊值配置，不应用于其他字段。", "meaning": "官方字段说明未解释此值，不推断实际就业时长。" } }, "labels": { "TARGET": { "definition": "Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases)", "positive_class": "unknown", "negative_class": "unknown", "performance_window": "X/Y 未公开；不推断 bad30/bad60", "field": "TARGET", "simulated": false, "available_in": "application_train.csv", "unavailable_in": "application_test.csv", "unobserved_value": null } } }, "limitations": [ "只组织已有统计，未计算表不在目录中；失败和跳过见 parameters.diagnostics / fit_failures。", "Null 不是数值 0；风险指标需结合标签表现状态，样本分布使用全样本。", "比较前核对样本范围、权重、缺失/特殊箱、拟合来源、排序口径及参考来源；关联不代表因果。" ] }, "queries": [ { "question": "Excel 首行实际值与快照对齐", "reference": { "report_id": "d73396cc-4ad6-493e-a69a-0cd2a0a435e6", "table": "detail", "query": { "offset": 0, "limit": 1 } }, "total_rows": 3257, "returned_rows": 1, "omitted_rows": 3256, "next_offset": 1, "rows": [ { "y": "TARGET", "feature": "AMT_ANNUITY", "display_name": "贷款年金", "dtype": "Float64", "data_source": "Home Credit 2018 application_{train|test}.csv", "trend": "peak", "mars_group": "0train", "bin_index": 0, "bin_label": "00_[-inf, 16,515)", "count": 53823.0, "observed_count": 53823.0, "bad": 3800.0, "good": 50023.0, "pct": 0.2499048167373952, "bad_rate": 0.07060178733998476, "lift": 0.8706435354000479, "cum_count": 53823.0, "cum_observed_count": 53823.0, "cum_bad": 3800.0, "cum_bad_rate": 0.07060178733998476, "psi_bin": 0.0, "ks_bin": 3.521493713732288, "auc_bin": 0.02749724608806191, "iv_bin": 0.005272458307445049, "total_count": 215374.0, "bin_type": "首尾组", "tot_amt": 12147315133.5, "good_amt": 11285842158.0, "bad_amt": 861472975.5, "avg_amt": 225690.042054512, "amt_bad_rate": 0.07091879695491066, "lift_amt": 0.9399693647747305 } ] } ], "findings": { "formats": [ { "format": "HTML", "path": "binning.html", "use": "原生分箱图、指标与业务元数据交互" }, { "format": "Excel", "path": "binning.xlsx", "use": "同批分箱静态工作表" }, { "format": ".marsreport", "path": "binning.marsreport", "use": "目录、查询、保存统计的导出；附加交叉聚合回放另用 score-cross" }, { "format": "bounded JSON", "path": "agent-context.json", "use": "Unicode 字符预算内的目录与证据" }, { "format": "TXT", "path": "external-agent-task.txt", "use": "外部消费者提示材料；未执行 LLM" } ], "excel_sheets": [ "FeatureMetadata", "BusinessContext", "ReportSemantics", "000_summary", "001_detail", "002_trend.psi", "003_trend.auc", "004_trend.ks", "005_trend.iv", "006_trend.missing", "007_trend.lift", "008_trend.risk_corr", "009_calculation_status", "010_missing_by_day", "011_risk_corr_reference" ], "excel_key_values_match": true, "shared_cases": [ 2, 6, 7 ] }, "unavailable": "Excel 不恢复交互计算；快照不恢复任意 Python 对象。" }