MARS Data Profile

FeatureMetadata

featuredisplay_namedtypedata_sourcedescriptionunit
Identification if loan is cash or revolving未公开
Gender of the client未公开
Flag if the client owns a car未公开
Flag if client owns a house or flat未公开
Number of children the client has次数或人数(按官方定义)
Income of the client原始金额单位(币种未公开)
Loan annuity原始金额单位(币种未公开)
For consumer loans it is the price of the goods for which the loan is given原始金额单位(币种未公开)
Who was accompanying client when he was applying for the loan未公开
Clients income type (businessman, working, maternity leave,…)未公开
Level of highest education the client achieved未公开
Family status of the client未公开
What is the housing situation of the client (renting, living with parents, ...)未公开
Normalized population of region where client lives (higher number means the client lives in more populated region)归一化值(无量纲)
Client's age in days at the time of application天(相对申请日,保留原始符号)
How many days before the application the person started current employment天(相对申请日,保留原始符号)
How many days before the application did client change his registration天(相对申请日,保留原始符号)
How many days before the application did client change the identity document with which he applied for the loan天(相对申请日,保留原始符号)
Age of client's car未公开
Did client provide mobile phone (1=YES, 0=NO)未公开
Did client provide work phone (1=YES, 0=NO)未公开
Did client provide home phone (1=YES, 0=NO)未公开
Was mobile phone reachable (1=YES, 0=NO)未公开
Did client provide home phone (1=YES, 0=NO)未公开
Did client provide email (1=YES, 0=NO)未公开
What kind of occupation does the client have未公开
How many family members does client have次数或人数(按官方定义)
Our rating of the region where client lives (1,2,3)未公开
Our rating of the region where client lives with taking city into account (1,2,3)未公开
On which day of the week did the client apply for the loan未公开
Approximately at what hour did the client apply for the loan未公开
Flag if client's permanent address does not match contact address (1=different, 0=same, at region level)未公开
Flag if client's permanent address does not match work address (1=different, 0=same, at region level)未公开
Flag if client's contact address does not match work address (1=different, 0=same, at region level)未公开
Flag if client's permanent address does not match contact address (1=different, 0=same, at city level)未公开
Flag if client's permanent address does not match work address (1=different, 0=same, at city level)未公开
Flag if client's contact address does not match work address (1=different, 0=same, at city level)未公开
Type of organization where client works未公开
Normalized score from external data source归一化值(无量纲)
Normalized score from external data source归一化值(无量纲)
Normalized score from external data source归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
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归一化值(无量纲)
How many observation of client's social surroundings with observable 30 DPD (days past due) default次数或人数(按官方定义)
How many observation of client's social surroundings defaulted on 30 DPD (days past due)次数或人数(按官方定义)
How many observation of client's social surroundings with observable 60 DPD (days past due) default次数或人数(按官方定义)
How many observation of client's social surroundings defaulted on 60 (days past due) DPD次数或人数(按官方定义)
How many days before application did client change phone天(相对申请日,保留原始符号)
Did client provide document 2未公开
Did client provide document 3未公开
Did client provide document 4未公开
Did client provide document 5未公开
Did client provide document 6未公开
Did client provide document 7未公开
Did client provide document 8未公开
Did client provide document 9未公开
Did client provide document 10未公开
Did client provide document 11未公开
Did client provide document 12未公开
Did client provide document 13未公开
Did client provide document 14未公开
Did client provide document 15未公开
Did client provide document 16未公开
Did client provide document 17未公开
Did client provide document 18未公开
Did client provide document 19未公开
Did client provide document 20未公开
Did client provide document 21未公开
Number of enquiries to Credit Bureau about the client one hour before application次数或人数(按官方定义)
Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application)次数或人数(按官方定义)
Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application)次数或人数(按官方定义)
Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application)次数或人数(按官方定义)
Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application)次数或人数(按官方定义)
Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application)次数或人数(按官方定义)

BusinessContext

keyvaluepartparts
label_definition"unknown"11
observation_window"unknown"11
currency"unknown"11
dataset_id"home-credit-default-risk-2018"11
source"https://www.kaggle.com/competitions/home-credit-default-risk/data"11
real_features{"source":"application_train.csv/application_test.csv","simulated":false,"derived_features_added":false}11
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"}11
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。"}11
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"}11
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}11
weightsnull11
sample_scope"两个 application 文件的全部样本"11
preparation_version"home-credit-2018-v1"11
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。"}}11
labels{"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"}}11

ReportSemantics

keyvaluepartparts
report_type"MarsProfileReport"11
report_id"5d00f397-1768-44b0-9306-cd2531e7e9a6"11
format_version111
source{"kind":"analysis","producer":"mars","input_provenance":"caller_provided_dataframe"}11
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"}11
serialization{"non_finite":"tagged float: {$mars: float, value: nan/inf/-inf}","date":"ISO-8601","null":"missing; calculation state is separate"}11
tables{"overview":{"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"},"missing_rate":{"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"},"zeros_rate":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"},"mean":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"}}},"calculation_status":{"rows":16,"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"},"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"}}},"dq.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"}}},"dq.zeros":{"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":"原始零值数/全样本数,非数值字段为 0"},"1val":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"},"2test":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"},"3oot":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"},"4_oot_undue":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"},"total":{"dtype":"Float64","unit":"ratio","meaning":"原始零值数/全样本数,非数值字段为 0"}}},"stats.mean":{"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":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"},"1val":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"},"2test":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"},"3oot":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"},"4_oot_undue":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"},"total":{"dtype":"Float64","unit":"unknown_business_unit","meaning":"非加权有效值均值;排除 Null、NaN、配置缺失值和特殊值;非数值字段未计算"}}},"stats.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"}}}}11
calculation_state{"table":"calculation_status","diagnostics":"parameters.diagnostics/fit_failures/raw_ks_diagnostics","scope":"feature/target/group/metric; profile status contains exceptional outcomes only, not all trend cells"}11
parameters{"mars_version":"0.0.28","polars_version":"1.42.0","generated_at_utc":"2026-10-15T14:46:46.380326+00:00","input_type":"polars","row_count":356255,"column_count":124,"features":["NAME_CONTRACT_TYPE","CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","CNT_CHILDREN","AMT_INCOME_TOTAL","AMT_ANNUITY","AMT_GOODS_PRICE","NAME_TYPE_SUITE","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE","NAME_FAMILY_STATUS","NAME_HOUSING_TYPE","REGION_POPULATION_RELATIVE","DAYS_BIRTH","DAYS_EMPLOYED","DAYS_REGISTRATION","DAYS_ID_PUBLISH","OWN_CAR_AGE","FLAG_MOBIL","FLAG_EMP_PHONE","FLAG_WORK_PHONE","FLAG_CONT_MOBILE","FLAG_PHONE","FLAG_EMAIL","OCCUPATION_TYPE","CNT_FAM_MEMBERS","REGION_RATING_CLIENT","REGION_RATING_CLIENT_W_CITY","WEEKDAY_APPR_PROCESS_START","HOUR_APPR_PROCESS_START","REG_REGION_NOT_LIVE_REGION","REG_REGION_NOT_WORK_REGION","LIVE_REGION_NOT_WORK_REGION","REG_CITY_NOT_LIVE_CITY","REG_CITY_NOT_WORK_CITY","LIVE_CITY_NOT_WORK_CITY","ORGANIZATION_TYPE","EXT_SOURCE_1","EXT_SOURCE_2","EXT_SOURCE_3","APARTMENTS_AVG","BASEMENTAREA_AVG","YEARS_BEGINEXPLUATATION_AVG","YEARS_BUILD_AVG","COMMONAREA_AVG","ELEVATORS_AVG","ENTRANCES_AVG","FLOORSMAX_AVG","FLOORSMIN_AVG","LANDAREA_AVG","LIVINGAPARTMENTS_AVG","LIVINGAREA_AVG","NONLIVINGAPARTMENTS_AVG","NONLIVINGAREA_AVG","APARTMENTS_MODE","BASEMENTAREA_MODE","YEARS_BEGINEXPLUATATION_MODE","YEARS_BUILD_MODE","COMMONAREA_MODE","ELEVATORS_MODE","ENTRANCES_MODE","FLOORSMAX_MODE","FLOORSMIN_MODE","LANDAREA_MODE","LIVINGAPARTMENTS_MODE","LIVINGAREA_MODE","NONLIVINGAPARTMENTS_MODE","NONLIVINGAREA_MODE","APARTMENTS_MEDI","BASEMENTAREA_MEDI","YEARS_BEGINEXPLUATATION_MEDI","YEARS_BUILD_MEDI","COMMONAREA_MEDI","ELEVATORS_MEDI","ENTRANCES_MEDI","FLOORSMAX_MEDI","FLOORSMIN_MEDI","LANDAREA_MEDI","LIVINGAPARTMENTS_MEDI","LIVINGAREA_MEDI","NONLIVINGAPARTMENTS_MEDI","NONLIVINGAREA_MEDI","FONDKAPREMONT_MODE","HOUSETYPE_MODE","TOTALAREA_MODE","WALLSMATERIAL_MODE","EMERGENCYSTATE_MODE","OBS_30_CNT_SOCIAL_CIRCLE","DEF_30_CNT_SOCIAL_CIRCLE","OBS_60_CNT_SOCIAL_CIRCLE","DEF_60_CNT_SOCIAL_CIRCLE","DAYS_LAST_PHONE_CHANGE","FLAG_DOCUMENT_2","FLAG_DOCUMENT_3","FLAG_DOCUMENT_4","FLAG_DOCUMENT_5","FLAG_DOCUMENT_6","FLAG_DOCUMENT_7","FLAG_DOCUMENT_8","FLAG_DOCUMENT_9","FLAG_DOCUMENT_10","FLAG_DOCUMENT_11","FLAG_DOCUMENT_12","FLAG_DOCUMENT_13","FLAG_DOCUMENT_14","FLAG_DOCUMENT_15","FLAG_DOCUMENT_16","FLAG_DOCUMENT_17","FLAG_DOCUMENT_18","FLAG_DOCUMENT_19","FLAG_DOCUMENT_20","FLAG_DOCUMENT_21","AMT_REQ_CREDIT_BUREAU_HOUR","AMT_REQ_CREDIT_BUREAU_DAY","AMT_REQ_CREDIT_BUREAU_WEEK","AMT_REQ_CREDIT_BUREAU_MON","AMT_REQ_CREDIT_BUREAU_QRT","AMT_REQ_CREDIT_BUREAU_YEAR"],"feature_dtypes":{"NAME_CONTRACT_TYPE":"String","CODE_GENDER":"String","FLAG_OWN_CAR":"String","FLAG_OWN_REALTY":"String","CNT_CHILDREN":"Int64","AMT_INCOME_TOTAL":"Float64","AMT_ANNUITY":"Float64","AMT_GOODS_PRICE":"Float64","NAME_TYPE_SUITE":"String","NAME_INCOME_TYPE":"String","NAME_EDUCATION_TYPE":"String","NAME_FAMILY_STATUS":"String","NAME_HOUSING_TYPE":"String","REGION_POPULATION_RELATIVE":"Float64","DAYS_BIRTH":"Int64","DAYS_EMPLOYED":"Int64","DAYS_REGISTRATION":"Float64","DAYS_ID_PUBLISH":"Int64","OWN_CAR_AGE":"Float64","FLAG_MOBIL":"Int64","FLAG_EMP_PHONE":"Int64","FLAG_WORK_PHONE":"Int64","FLAG_CONT_MOBILE":"Int64","FLAG_PHONE":"Int64","FLAG_EMAIL":"Int64","OCCUPATION_TYPE":"String","CNT_FAM_MEMBERS":"Float64","REGION_RATING_CLIENT":"Int64","REGION_RATING_CLIENT_W_CITY":"Int64","WEEKDAY_APPR_PROCESS_START":"String","HOUR_APPR_PROCESS_START":"Int64","REG_REGION_NOT_LIVE_REGION":"Int64","REG_REGION_NOT_WORK_REGION":"Int64","LIVE_REGION_NOT_WORK_REGION":"Int64","REG_CITY_NOT_LIVE_CITY":"Int64","REG_CITY_NOT_WORK_CITY":"Int64","LIVE_CITY_NOT_WORK_CITY":"Int64","ORGANIZATION_TYPE":"String","EXT_SOURCE_1":"Float64","EXT_SOURCE_2":"Float64","EXT_SOURCE_3":"Float64","APARTMENTS_AVG":"Float64","BASEMENTAREA_AVG":"Float64","YEARS_BEGINEXPLUATATION_AVG":"Float64","YEARS_BUILD_AVG":"Float64","COMMONAREA_AVG":"Float64","ELEVATORS_AVG":"Float64","ENTRANCES_AVG":"Float64","FLOORSMAX_AVG":"Float64","FLOORSMIN_AVG":"Float64","LANDAREA_AVG":"Float64","LIVINGAPARTMENTS_AVG":"Float64","LIVINGAREA_AVG":"Float64","NONLIVINGAPARTMENTS_AVG":"Float64","NONLIVINGAREA_AVG":"Float64","APARTMENTS_MODE":"Float64","BASEMENTAREA_MODE":"Float64","YEARS_BEGINEXPLUATATION_MODE":"Float64","YEARS_BUILD_MODE":"Float64","COMMONAREA_MODE":"Float64","ELEVATORS_MODE":"Float64","ENTRANCES_MODE":"Float64","FLOORSMAX_MODE":"Float64","FLOORSMIN_MODE":"Float64","LANDAREA_MODE":"Float64","LIVINGAPARTMENTS_MODE":"Float64","LIVINGAREA_MODE":"Float64","NONLIVINGAPARTMENTS_MODE":"Float64","NONLIVINGAREA_MODE":"Float64","APARTMENTS_MEDI":"Float64","BASEMENTAREA_MEDI":"Float64","YEARS_BEGINEXPLUATATION_MEDI":"Float64","YEARS_BUILD_MEDI":"Float64","COMMONAREA_MEDI":"Float64","ELEVATORS_MEDI":"Float64","ENTRANCES_MEDI":"Float64","FLOORSMAX_MEDI":"Float64","FLOORSMIN_MEDI":"Float64","LANDAREA_MEDI":"Float64","LIVINGAPARTMENTS_MEDI":"Float64","LIVINGAREA_MEDI":"Float64","NONLIVINGAPARTMENTS_MEDI":"Float64","NONLIVINGAREA_MEDI":"Float64","FONDKAPREMONT_MODE":"String","HOUSETYPE_MODE":"String","TOTALAREA_MODE":"Float64","WALLSMATERIAL_MODE":"String","EMERGENCYSTATE_MODE":"String","OBS_30_CNT_SOCIAL_CIRCLE":"Float64","DEF_30_CNT_SOCIAL_CIRCLE":"Float64","OBS_60_CNT_SOCIAL_CIRCLE":"Float64","DEF_60_CNT_SOCIAL_CIRCLE":"Float64","DAYS_LAST_PHONE_CHANGE":"Float64","FLAG_DOCUMENT_2":"Int64","FLAG_DOCUMENT_3":"Int64","FLAG_DOCUMENT_4":"Int64","FLAG_DOCUMENT_5":"Int64","FLAG_DOCUMENT_6":"Int64","FLAG_DOCUMENT_7":"Int64","FLAG_DOCUMENT_8":"Int64","FLAG_DOCUMENT_9":"Int64","FLAG_DOCUMENT_10":"Int64","FLAG_DOCUMENT_11":"Int64","FLAG_DOCUMENT_12":"Int64","FLAG_DOCUMENT_13":"Int64","FLAG_DOCUMENT_14":"Int64","FLAG_DOCUMENT_15":"Int64","FLAG_DOCUMENT_16":"Int64","FLAG_DOCUMENT_17":"Int64","FLAG_DOCUMENT_18":"Int64","FLAG_DOCUMENT_19":"Int64","FLAG_DOCUMENT_20":"Int64","FLAG_DOCUMENT_21":"Int64","AMT_REQ_CREDIT_BUREAU_HOUR":"Float64","AMT_REQ_CREDIT_BUREAU_DAY":"Float64","AMT_REQ_CREDIT_BUREAU_WEEK":"Float64","AMT_REQ_CREDIT_BUREAU_MON":"Float64","AMT_REQ_CREDIT_BUREAU_QRT":"Float64","AMT_REQ_CREDIT_BUREAU_YEAR":"Float64"},"metrics":["missing","zeros","mean","psi"],"group_col":"dataset_flag","time_col":null,"time_grain":null,"benchmark_row_count":215374,"categorical_features":["NAME_CONTRACT_TYPE","CODE_GENDER","FLAG_OWN_CAR","FLAG_OWN_REALTY","NAME_TYPE_SUITE","NAME_INCOME_TYPE","NAME_EDUCATION_TYPE","NAME_FAMILY_STATUS","NAME_HOUSING_TYPE","OCCUPATION_TYPE","WEEKDAY_APPR_PROCESS_START","ORGANIZATION_TYPE","FONDKAPREMONT_MODE","HOUSETYPE_MODE","WALLSMATERIAL_MODE","EMERGENCYSTATE_MODE"],"profile_config":{"missing_values":[],"special_values":[],"overview_batch_size":500,"psi_batch_size":50,"psi_remove_empty_bins":true,"psi_merge_small_bins":true,"psi_min_bin_size":0.02,"psi_cv_ignore_threshold":0.05,"enable_sparkline":false,"sparkline_sample_size":200000,"sparkline_bins":8,"psi_n_bins":10,"psi_bin_method":"quantile","psi_include_missing":false,"psi_include_special":false},"diagnostics":[]}11
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。"]11

overview

featuredisplay_namedtypedata_sourcemissing_ratezeros_ratemean
0.01%0.00%27425.56
0.08%0.00%528020.00
0.00%0.00%170116.06
13.35%86.21%0.01
13.35%86.17%0.01
13.35%74.27%0.23
13.35%67.08%0.30
13.35%84.22%0.03
13.35%23.20%1.91
50.51%0.24%0.12
50.51%0.25%0.12
50.51%0.32%0.11
58.27%4.77%0.09
58.27%4.85%0.09
58.27%5.38%0.09
0.00%0.00%2.15
69.71%2.76%0.05
69.71%2.84%0.04
69.71%3.17%0.04
0.00%12.20%-978.58
0.00%0.03%-4983.59
0.29%88.28%0.14
0.29%91.32%0.10
53.07%27.84%0.08
53.07%28.28%0.08
53.07%29.10%0.08
50.08%0.10%0.15
50.08%0.10%0.15
50.08%0.12%0.15
54.43%0.00%0.50
0.19%0.00%0.51
19.55%0.00%0.51
49.50%0.94%0.23
49.50%0.96%0.23
49.50%1.10%0.22
67.68%0.76%0.23
67.68%0.77%0.23
67.68%0.83%0.23
59.18%5.12%0.07
59.18%5.23%0.07
59.18%5.73%0.07
68.20%0.13%0.10
68.20%0.13%0.10
68.20%0.16%0.11
49.94%0.09%0.11
49.94%0.10%0.11
49.94%0.14%0.11
69.29%17.79%0.01
69.29%18.31%0.01
69.29%19.34%0.01
54.95%19.14%0.03
54.95%19.88%0.03
54.95%21.91%0.03
0.29%53.31%1.43
0.29%53.56%1.41
66.03%0.63%12.02
0.00%0.00%0.02
48.01%0.18%0.10
48.52%0.16%0.98
48.52%0.17%0.98
48.52%0.06%0.98
66.33%0.03%0.75
66.33%0.03%0.76
66.33%0.03%0.76
0.00%70.19%0.41
0.00%0.00%-16041.25
0.00%0.00%64317.23
0.00%0.01%-3002.07
0.00%0.18%1.00
0.00%100.00%0.00
0.00%99.65%0.00
0.00%100.00%0.00
0.00%99.70%0.00
0.00%99.75%0.00
0.00%99.90%0.00
0.00%99.14%0.01
0.00%99.98%0.00
0.00%99.28%0.01
0.00%99.95%0.00
0.00%100.00%0.00
0.00%99.96%0.00
0.00%99.97%0.00
0.00%27.95%0.72
0.00%99.99%0.00
0.00%98.49%0.02
0.00%91.20%0.09
0.00%99.98%0.00
0.00%91.77%0.08
0.00%99.60%0.00
0.00%92.88%0.07
0.00%18.15%0.82
0.00%0.00%1.00
0.00%72.14%0.28
0.00%79.99%0.20
0.00%0.01%12.06
0.00%82.12%0.18
0.00%95.92%0.04
0.00%0.00%2.05
0.00%0.00%2.03
0.00%92.19%0.08
0.00%77.03%0.23
0.00%98.44%0.02
0.00%94.86%0.05
0.00%0.00%—
47.15%0.00%—
0.00%0.00%—
0.00%0.00%—
68.24%0.00%—
49.94%0.00%—
0.00%0.00%—
0.00%0.00%—
0.00%0.00%—
0.00%0.00%—
0.00%0.00%—
0.62%0.00%—
31.44%0.00%—
0.00%0.00%—
50.59%0.00%—
0.00%0.00%—

dq.missing

featuredisplay_namedtypedata_source0train1val2test3oot4_oot_unduetotal
0.00%0.01%0.00%0.01%0.05%0.01%
0.09%0.10%0.08%0.10%0.00%0.08%
0.00%0.00%0.00%0.00%0.00%0.00%
13.61%13.16%13.42%13.16%12.41%13.35%
13.61%13.16%13.42%13.16%12.41%13.35%
13.61%13.16%13.42%13.16%12.41%13.35%
13.61%13.16%13.42%13.16%12.41%13.35%
13.61%13.16%13.42%13.16%12.41%13.35%
13.61%13.16%13.42%13.16%12.41%13.35%
50.74%50.76%51.09%50.47%49.01%50.51%
50.74%50.76%51.09%50.47%49.01%50.51%
50.74%50.76%51.09%50.47%49.01%50.51%
58.52%58.31%58.94%58.30%56.71%58.27%
58.52%58.31%58.94%58.30%56.71%58.27%
58.52%58.31%58.94%58.30%56.71%58.27%
0.00%0.00%0.00%0.00%0.00%0.00%
69.87%69.61%70.30%69.76%68.72%69.71%
69.87%69.61%70.30%69.76%68.72%69.71%
69.87%69.61%70.30%69.76%68.72%69.71%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.32%0.38%0.38%0.31%0.06%0.29%
0.32%0.38%0.38%0.31%0.06%0.29%
53.31%53.38%53.56%52.89%51.68%53.07%
53.31%53.38%53.56%52.89%51.68%53.07%
53.31%53.38%53.56%52.89%51.68%53.07%
50.36%50.25%50.77%49.98%48.37%50.08%
50.36%50.25%50.77%49.98%48.37%50.08%
50.36%50.25%50.77%49.98%48.37%50.08%
56.34%56.92%56.21%56.28%42.12%54.43%
0.21%0.21%0.20%0.28%0.02%0.19%
19.95%19.46%19.79%19.38%17.78%19.55%
49.76%49.72%50.11%49.43%47.84%49.50%
49.76%49.72%50.11%49.43%47.84%49.50%
49.76%49.72%50.11%49.43%47.84%49.50%
67.83%67.52%68.33%67.83%66.61%67.68%
67.83%67.52%68.33%67.83%66.61%67.68%
67.83%67.52%68.33%67.83%66.61%67.68%
59.38%59.20%59.81%59.11%57.96%59.18%
59.38%59.20%59.81%59.11%57.96%59.18%
59.38%59.20%59.81%59.11%57.96%59.18%
68.36%68.04%68.63%68.35%67.25%68.20%
68.36%68.04%68.63%68.35%67.25%68.20%
68.36%68.04%68.63%68.35%67.25%68.20%
50.19%50.17%50.51%49.93%48.32%49.94%
50.19%50.17%50.51%49.93%48.32%49.94%
50.19%50.17%50.51%49.93%48.32%49.94%
69.45%69.19%69.63%69.39%68.41%69.29%
69.45%69.19%69.63%69.39%68.41%69.29%
69.45%69.19%69.63%69.39%68.41%69.29%
55.20%55.01%55.57%54.82%53.51%54.95%
55.20%55.01%55.57%54.82%53.51%54.95%
55.20%55.01%55.57%54.82%53.51%54.95%
0.32%0.38%0.38%0.31%0.06%0.29%
0.32%0.38%0.38%0.31%0.06%0.29%
65.90%66.12%66.24%66.26%66.29%66.03%
0.00%0.00%0.00%0.00%0.00%0.00%
48.27%48.25%48.61%47.95%46.41%48.01%
48.80%48.72%49.07%48.44%46.89%48.52%
48.80%48.72%49.07%48.44%46.89%48.52%
48.80%48.72%49.07%48.44%46.89%48.52%
66.49%66.13%66.89%66.52%65.28%66.33%
66.49%66.13%66.89%66.52%65.28%66.33%
66.49%66.13%66.89%66.52%65.28%66.33%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
47.39%47.40%47.68%47.18%45.56%47.15%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
68.42%67.89%68.77%68.27%67.28%68.24%
50.18%50.16%50.51%49.83%48.46%49.94%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.42%0.40%0.41%0.44%1.87%0.62%
31.37%31.73%31.00%31.14%32.01%31.44%
0.00%0.00%0.00%0.00%0.00%0.00%
50.85%50.72%51.27%50.46%49.02%50.59%
0.00%0.00%0.00%0.00%0.00%0.00%

dq.zeros

featuredisplay_namedtypedata_source0train1val2test3oot4_oot_unduetotal
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
85.91%86.36%86.07%86.36%87.45%86.21%
85.86%86.33%86.01%86.30%87.41%86.17%
72.22%72.67%71.85%72.61%86.86%74.27%
69.91%70.38%70.18%70.58%48.33%67.08%
83.63%83.97%83.72%84.14%87.35%84.22%
23.36%23.24%23.57%23.15%22.24%23.20%
0.24%0.23%0.27%0.25%0.25%0.24%
0.25%0.23%0.28%0.26%0.25%0.25%
0.31%0.30%0.35%0.32%0.30%0.32%
4.83%4.67%4.78%4.68%4.60%4.77%
4.91%4.73%4.84%4.79%4.68%4.85%
5.43%5.19%5.40%5.39%5.25%5.38%
0.00%0.00%0.00%0.00%0.00%0.00%
2.74%2.77%2.78%2.72%2.82%2.76%
2.82%2.88%2.84%2.81%2.92%2.84%
3.14%3.15%3.23%3.13%3.29%3.17%
12.22%12.34%12.31%12.34%11.90%12.20%
0.03%0.03%0.02%0.03%0.03%0.03%
88.24%88.35%88.15%88.17%88.62%88.28%
91.28%91.32%91.32%91.29%91.53%91.32%
27.91%27.67%27.87%27.83%27.63%27.84%
28.33%28.08%28.34%28.28%28.18%28.28%
29.12%28.87%29.18%29.12%29.07%29.10%
0.11%0.07%0.11%0.09%0.08%0.10%
0.11%0.07%0.12%0.09%0.08%0.10%
0.13%0.09%0.13%0.12%0.11%0.12%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.96%0.86%0.97%1.00%0.86%0.94%
0.98%0.87%1.00%1.02%0.87%0.96%
1.11%1.00%1.21%1.16%1.05%1.10%
0.76%0.77%0.80%0.69%0.80%0.76%
0.77%0.77%0.81%0.71%0.81%0.77%
0.82%0.84%0.84%0.76%0.88%0.83%
5.07%5.15%5.02%5.04%5.45%5.12%
5.18%5.27%5.12%5.15%5.56%5.23%
5.68%5.71%5.59%5.70%6.10%5.73%
0.13%0.14%0.15%0.17%0.09%0.13%
0.14%0.14%0.15%0.17%0.10%0.13%
0.16%0.16%0.20%0.20%0.14%0.16%
0.09%0.09%0.11%0.09%0.09%0.09%
0.10%0.09%0.11%0.09%0.10%0.10%
0.14%0.14%0.16%0.14%0.14%0.14%
17.76%17.87%17.69%17.51%18.08%17.79%
18.25%18.40%18.21%18.04%18.70%18.31%
19.27%19.37%19.23%19.23%19.77%19.34%
19.10%19.19%18.95%19.15%19.43%19.14%
19.81%19.93%19.71%19.91%20.24%19.88%
21.82%21.80%21.68%22.06%22.41%21.91%
53.32%53.31%53.05%53.40%53.39%53.31%
53.57%53.54%53.35%53.63%53.63%53.56%
0.70%0.69%0.68%0.69%0.24%0.63%
0.00%0.00%0.00%0.00%0.00%0.00%
0.18%0.20%0.25%0.17%0.15%0.18%
0.16%0.16%0.20%0.19%0.12%0.16%
0.17%0.16%0.21%0.20%0.13%0.17%
0.04%0.06%0.06%0.05%0.16%0.06%
0.04%0.02%0.02%0.04%0.04%0.03%
0.04%0.02%0.02%0.04%0.04%0.03%
0.04%0.03%0.02%0.04%0.03%0.03%
69.99%70.14%69.95%70.35%71.16%70.19%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.01%0.00%0.01%0.00%0.01%0.01%
0.18%0.20%0.20%0.21%0.16%0.18%
100.00%100.00%100.00%100.00%100.00%100.00%
99.61%99.63%99.65%99.54%99.88%99.65%
100.00%100.00%100.00%100.00%100.00%100.00%
99.64%99.69%99.67%99.65%100.00%99.70%
99.72%99.75%99.63%99.66%100.00%99.75%
99.88%99.88%99.88%99.91%100.00%99.90%
99.00%99.02%99.06%99.03%100.00%99.14%
99.98%99.97%99.97%99.97%100.00%99.98%
99.18%99.26%99.18%99.18%99.84%99.28%
99.94%99.95%99.93%99.93%100.00%99.95%
99.99%100.00%100.00%100.00%100.00%100.00%
99.95%99.92%99.96%99.94%100.00%99.96%
99.96%99.97%99.97%99.97%100.00%99.97%
29.02%29.22%28.35%29.29%21.34%27.95%
99.99%99.99%99.99%100.00%99.99%99.99%
98.51%98.37%98.37%98.59%98.52%98.49%
91.16%91.16%91.34%91.29%91.25%91.20%
99.98%99.98%99.96%99.98%100.00%99.98%
91.87%91.74%92.40%91.43%91.15%91.77%
99.60%99.63%99.62%99.64%99.55%99.60%
94.31%94.40%94.24%94.44%83.74%92.88%
17.97%18.26%18.04%18.00%19.03%18.15%
0.00%0.00%0.00%0.00%0.00%0.00%
71.88%71.98%71.90%71.91%73.69%72.14%
80.05%79.91%80.16%80.18%79.53%79.99%
0.01%0.01%0.03%0.02%0.02%0.01%
82.05%81.84%82.16%82.06%82.58%82.12%
95.93%95.76%96.21%95.89%95.80%95.92%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
92.19%92.25%92.11%92.16%92.25%92.19%
76.99%76.79%76.82%77.03%77.53%77.03%
98.47%98.57%98.42%98.55%98.12%98.44%
94.91%94.86%95.12%94.87%94.48%94.86%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%
0.00%0.00%0.00%0.00%0.00%0.00%

stats.mean

featuredisplay_namedtypedata_source0train1val2test3oot4_oot_unduetotal
27110.4627095.3027118.3027098.9329426.2427425.56
538191.70538486.89537796.83540335.13462618.84528020.00
168930.44168170.84169114.12168181.14178431.81170116.06
0.010.010.010.010.000.01
0.010.010.010.010.000.01
0.270.270.270.270.010.23
0.270.260.260.260.550.30
0.030.040.030.030.000.03
1.901.891.901.901.981.91
0.120.120.120.120.120.12
0.120.120.120.120.120.12
0.110.120.120.110.120.11
0.090.090.090.090.090.09
0.090.090.090.090.090.09
0.090.090.090.090.090.09
2.152.152.152.152.152.15
0.040.050.040.040.050.05
0.040.050.040.040.050.04
0.040.040.040.040.050.04
-961.32-968.73-961.87-968.78-1077.77-978.58
-4982.69-5015.35-4980.16-4986.90-4967.65-4983.59
0.140.140.140.140.140.14
0.100.100.100.100.100.10
0.080.080.080.080.090.08
0.080.080.080.080.080.08
0.070.080.080.070.080.08
0.150.150.150.150.150.15
0.150.150.150.150.150.15
0.140.150.150.150.150.15
0.500.500.500.500.500.50
0.510.520.510.510.520.51
0.510.510.510.510.500.51
0.230.230.220.230.230.23
0.230.230.220.230.230.23
0.220.220.220.220.230.22
0.230.230.230.230.240.23
0.230.230.230.230.240.23
0.230.230.230.230.230.23
0.070.070.070.070.070.07
0.070.070.070.070.070.07
0.060.060.070.070.070.07
0.100.100.100.100.110.10
0.100.100.100.100.110.10
0.110.110.110.110.110.11
0.110.110.110.110.110.11
0.110.110.110.110.110.11
0.110.110.110.110.110.11
0.010.010.010.010.010.01
0.010.010.010.010.010.01
0.010.010.010.010.010.01
0.030.030.030.030.030.03
0.030.030.030.030.030.03
0.030.030.030.030.030.03
1.421.421.431.421.451.43
1.401.411.421.401.441.41
12.0612.1111.8512.2411.7912.02
0.020.020.020.020.020.02
0.100.100.100.100.110.10
0.980.980.980.980.980.98
0.980.980.980.980.980.98
0.980.980.980.980.980.98
0.750.750.750.750.750.75
0.760.760.760.760.750.76
0.760.760.760.760.760.76
0.420.410.420.410.400.41
-16033.19-16069.68-16023.26-16044.71-16068.08-16041.25
63680.7464695.4263909.6463781.8967485.3764317.23
-2994.99-2988.77-2992.92-2995.38-3051.71-3002.07
1.001.001.001.001.001.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.010.010.010.010.000.01
0.000.000.000.000.000.00
0.010.010.010.010.000.01
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.710.710.720.710.790.72
0.000.000.000.000.000.00
0.010.020.020.010.010.02
0.090.090.090.090.090.09
0.000.000.000.000.000.00
0.080.080.080.090.090.08
0.000.000.000.000.000.00
0.060.060.060.060.160.07
0.820.820.820.820.810.82
1.001.001.001.001.001.00
0.280.280.280.280.260.28
0.200.200.200.200.200.20
12.0712.0912.0312.0512.0112.06
0.180.180.180.180.170.18
0.040.040.040.040.040.04
2.052.052.062.062.042.05
2.032.032.042.042.012.03
0.080.080.080.080.080.08
0.230.230.230.230.220.23
0.020.010.020.010.020.02
0.050.050.050.050.060.05
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stats.psi

featuredisplay_namedtypedata_source0train1val2test3oot4_oot_unduetotal
0.000.000.000.000.030.00
0.000.000.000.000.080.00
0.000.000.000.000.020.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.490.00
0.000.000.000.000.320.01
0.000.000.000.000.070.00
0.000.000.000.000.010.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.010.00
0.000.000.000.000.070.00
0.000.000.000.000.070.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.010.00
0.000.000.000.000.010.00
0.000.000.000.000.010.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.030.00
0.000.000.000.000.00-0.00
0.000.000.000.000.00-0.00
0.000.000.000.000.000.00
0.000.000.000.000.00-0.00
0.000.000.000.000.000.00
0.000.000.000.000.00-0.00
0.000.000.000.000.120.00
0.000.000.000.000.000.00
0.000.000.000.000.00-0.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.210.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.050.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.00-0.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.010.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00
0.000.000.000.000.000.00

No calculated tables available.

calculation_status

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