ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)

ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)相关文章ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)ML之FE：基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(手动设计新特征、利用featuretools工具实现自动特征生成)实现基于自定义数据集(银行客户信息贷款和赔偿)对比实现特征衍生(T1手动设计新特征、T2利用featuretools工具实现自动特征生成)设计思路

输出结果clients client_id age education income credit_score loan_type joined0 44966 31 BachelorDegree 58530 822 credit 2019-05-131 46602 24 DoctorDegree 176949 583 cash 2020-03-142 38435 43 DoctorDegree 183813 596 home 2019-06-263 47641 48 DoctorDegree 149357 532 credit 2020-06-254 25942 18 DoctorDegree 227219 839 cash 2019-06-09loans client_id loan_type loan_amount repaid loan_id loan_start loan_end 0 44966 credit 7733 0 11246 2020-09-15 2023-02-22 1 46602 cash 9547 1 10612 2019-01-25 2021-09-22 2 38435 home 6615 0 10339 2019-05-13 2020-12-25 3 47641 credit 5050 1 10442 2020-02-22 2022-10-26 4 25942 cash 14059 0 11871 2019-04-10 2021-03-29 rate 0 0.42 1 4.03 2 5.65 3 7.51 4 3.58 payments loan_id payment_amount payment_date missed0 11246 1136 2020-11-19 01 11246 858 2020-11-29 02 11246 1245 2020-12-31 03 11246 1153 2021-01-17 14 11246 838 2021-02-10 1++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ T1、手动设计新特征loan_typemean_loan_amountmax_loan_amountmin_loan_amountcash12014.5148848458credit7329.857143134573795home5456.5101301430other9025.833333141082466T2、利用featuretools工具实现自动特征生成将payments数据框全部加入实体集后，输出信息， Entity: payments Variables: payment_id (dtype: index) loan_id (dtype: numeric) payment_amount (dtype: numeric) payment_date (dtype: datetime_time_index) missed (dtype: categorical) Shape: (Rows: 179, Columns: 5)该实体集现在包括三个实体以及连接这些实体之间的关系，这时候已经做好了构造新特征的准备。 Entityset: clients Entities: clients [Rows: 25, Columns: 7] payments [Rows: 179, Columns: 5] loans [Rows: 25, Columns: 8] Relationships: loans.client_id -> clients.client_id payments.loan_id -> loans.loan_idprimitives: <class 'pandas.core.frame.DataFrame'> 79 T2.1、79个Feature Primitives[79 rows x 5 columns]nametypedask_compatiblekoalas_compatibledescription描述0allaggregationTRUEFALSECalculates if all values are 'True' in a list.计算列表中所有值是否均为“ True”。1num_uniqueaggregationTRUETRUEDetermines the number of distinct values, ignoring `NaN` values.确定不同值的数量，忽略“ NaN”值。2anyaggregationTRUEFALSEDetermines if any value is 'True' in a list.确定列表中是否有任何值为“ True”。3time_since_lastaggregationFALSEFALSECalculates the time elapsed since the last datetime (default in seconds).计算自上一个日期时间以来经过的时间（默认值以秒为单位）。4n_most_commonaggregationFALSEFALSEDetermines the `n` most common elements.确定n个最常见的元素。5lastaggregationFALSEFALSEDetermines the last value in a list.确定列表中的最后一个值。6entropyaggregationFALSEFALSECalculates the entropy for a categorical variable计算分类变量的熵7num_trueaggregationTRUEFALSECounts the number of `True` values.计算“True”值的数量。8medianaggregationFALSEFALSEDetermines the middlemost number in a list of values.确定值列表中的最中间数字。9skewaggregationFALSEFALSEComputes the extent to which a distribution differs from a normal distribution.计算分布与正态分布的差异程度。10maxaggregationTRUETRUECalculates the highest value, ignoring `NaN` values.计算最高值，而忽略“ NaN”值。11avg_time_betweenaggregationFALSEFALSEComputes the average number of seconds between consecutive events.计算连续事件之间的平均秒数。12firstaggregationFALSEFALSEDetermines the first value in a list.确定列表中的第一个值。13sumaggregationTRUETRUECalculates the total addition, ignoring `NaN`.计算总的加法，忽略“ NaN”。14minaggregationTRUETRUECalculates the smallest value, ignoring `NaN` values.计算最小值，忽略“ NaN”值。15meanaggregationTRUETRUEComputes the average for a list of values.计算值列表的平均值。16stdaggregationTRUETRUEComputes the dispersion relative to the mean value, ignoring `NaN`.忽略“ NaN”，计算相对于平均值的离散度。17time_since_firstaggregationFALSEFALSECalculates the time elapsed since the first datetime (in seconds).计算自第一个日期时间以来经过的时间（以秒为单位）。18modeaggregationFALSEFALSEDetermines the most commonly repeated value.确定最常见的重复值。19percent_trueaggregationTRUEFALSEDetermines the percent of `True` values.确定“True”值的百分比。20countaggregationTRUETRUEDetermines the total number of values, excluding `NaN`.确定值的总数，不包括“ NaN”。21trendaggregationFALSEFALSECalculates the trend of a variable over time.计算变量随时间的趋势。22multiply_booleantransformTRUEFALSEElement-wise multiplication of two lists of boolean values.两个布尔值列表的逐元素相乘。23less_than_equal_totransformTRUETRUEDetermines if values in one list are less than or equal to another list.确定一个列表中的值是否小于或等于另一个列表。24percentiletransformFALSEFALSEDetermines the percentile rank for each value in a list.确定列表中每个值的百分等级。25num_characterstransformTRUETRUECalculates the number of characters in a string.计算字符串中的字符数。26daytransformTRUETRUEDetermines the day of the month from a datetime.从日期时间确定一个月中的哪一天。27cum_counttransformFALSEFALSECalculates the cumulative count.计算累积计数。28scalar_subtract_numeric_featuretransformTRUETRUESubtract each value in the list from a given scalar.从给定的标量中减去列表中的每个值。29time_since_previoustransformFALSEFALSECompute the time since the previous entry in a list.计算自列表中上一个条目以来的时间。30is_weekendtransformTRUETRUEDetermines if a date falls on a weekend.确定日期是否在周末。31yeartransformTRUETRUEDetermines the year value of a datetime.确定日期时间的年份值。32ortransformTRUETRUEElement-wise logical OR of two lists.两个列表的元素级逻辑或。33not_equaltransformTRUEFALSEDetermines if values in one list are not equal to another list.确定一个列表中的值是否不等于另一个列表。34monthtransformTRUETRUEDetermines the month value of a datetime.确定日期时间的月份值。35latitudetransformFALSEFALSEReturns the first tuple value in a list of LatLong tuples.返回LatLong元组列表中的第一个元组值。36modulo_by_featuretransformTRUETRUEReturn the modulo of a scalar by each element in the list.返回列表中每个元素的标量的模。37less_than_scalartransformTRUETRUEDetermines if values are less than a given scalar.确定值是否小于给定的标量。38less_than_equal_to_scalartransformTRUETRUEDetermines if values are less than or equal to a given scalar.确定值是否小于或等于给定的标量。39is_nulltransformTRUETRUEDetermines if a value is null.确定值是否为空。40modulo_numeric_scalartransformTRUETRUEReturn the modulo of each element in the list by a scalar.以标量返回列表中每个元素的模。41greater_than_equal_totransformTRUETRUEDetermines if values in one list are greater than or equal to another list.确定一个列表中的值是否大于或等于另一个列表。42difftransformFALSEFALSECompute the difference between the value in a list and the计算列表中的值与43divide_numeric_scalartransformTRUETRUEDivide each element in the list by a scalar.用标量除以列表中的每个元素。44modulo_numerictransformTRUETRUEElement-wise modulo of two lists.两个列表的按元素取模。45multiply_numeric_scalartransformTRUETRUEMultiply each element in the list by a scalar.将列表中的每个元素乘以标量。46divide_numerictransformTRUETRUEElement-wise division of two lists.两个列表的按元素划分。47equal_scalartransformTRUETRUEDetermines if values in a list are equal to a given scalar.确定列表中的值是否等于给定的标量。48agetransformTRUEFALSECalculates the age in years as a floating point number given a给定a，以浮点数形式计算年龄（以年为单位）。49secondtransformTRUETRUEDetermines the seconds value of a datetime.确定日期时间的秒值。50cum_meantransformFALSEFALSECalculates the cumulative mean.计算累积平均值。51multiply_numerictransformTRUETRUEElement-wise multiplication of two lists.两个列表的按元素乘法。52less_thantransformTRUETRUEDetermines if values in one list are less than another list.确定一个列表中的值是否小于另一个列表。53time_sincetransformTRUEFALSECalculates time from a value to a specified cutoff datetime.计算从值到指定的截止日期时间的时间。54weekdaytransformTRUETRUEDetermines the day of the week from a datetime.从日期时间确定星期几。55haversinetransformFALSEFALSECalculates the approximate haversine distance between two LatLong计算两个LatLong之间的大致Haversine距离56cum_sumtransformFALSEFALSECalculates the cumulative sum.计算累计和。57add_numeric_scalartransformTRUETRUEAdd a scalar to each value in the list.向列表中的每个值添加一个标量。58greater_than_scalartransformTRUETRUEDetermines if values are greater than a given scalar.确定值是否大于给定的标量。59num_wordstransformTRUETRUEDetermines the number of words in a string by counting the spaces.通过计算空格来确定字符串中的单词数。60minutetransformTRUETRUEDetermines the minutes value of a datetime.确定日期时间的分钟值。61absolutetransformTRUETRUEComputes the absolute value of a number.计算数字的绝对值。62andtransformTRUETRUEElement-wise logical AND of two lists.两个列表的按元素逻辑与。63equaltransformTRUETRUEDetermines if values in one list are equal to another list.确定一个列表中的值是否等于另一列表。64hourtransformTRUETRUEDetermines the hour value of a datetime.确定日期时间的小时值。65isintransformTRUETRUEDetermines whether a value is present in a provided list.确定提供的列表中是否存在值。66subtract_numeric_scalartransformTRUETRUESubtract a scalar from each element in the list.从列表中的每个元素中减去一个标量。67greater_than_equal_to_scalartransformTRUETRUEDetermines if values are greater than or equal to a given scalar.确定值是否大于或等于给定的标量。68nottransformTRUETRUENegates a boolean value.取反布尔值。69cum_maxtransformFALSEFALSECalculates the cumulative maximum.计算累计最大值。70subtract_numerictransformTRUEFALSEElement-wise subtraction of two lists.两个列表的逐元素减法。71greater_thantransformTRUEFALSEDetermines if values in one list are greater than another list.确定一个列表中的值是否大于另一个列表。72weektransformTRUETRUEDetermines the week of the year from a datetime.从日期时间确定一年中的星期。73add_numerictransformTRUETRUEElement-wise addition of two lists.按元素添加两个列表。74divide_by_featuretransformTRUETRUEDivide a scalar by each value in the list.将标量除以列表中的每个值。75not_equal_scalartransformTRUETRUEDetermines if values in a list are not equal to a given scalar.确定列表中的值是否不等于给定的标量。76longitudetransformFALSEFALSEReturns the second tuple value in a list of LatLong tuples.返回LatLong元组列表中的第二个元组值。77negatetransformTRUETRUENegates a numeric value.取反数值。78cum_mintransformFALSEFALSECalculates the cumulative minimum.计算累计最小值。T2.2、深度特征合成：指通过叠加多个基元来得到特征1)、完整的数据框包含了254-5个新特征，特征工具通过结合与叠加基元特征构造了许多新特征feature_names: <class 'list'> 254 [<Feature: age>, <Feature: education>, <Feature: income>, <Feature: credit_score>, <Feature: loan_type>, <Feature: LAST(loans.loan_amount)>, <Feature: LAST(loans.loan_id)>, <Feature: LAST(loans.loan_type)>, <Feature: LAST(loans.rate)>, <Feature: LAST(loans.repaid)>, <Feature: MAX(loans.loan_amount)>, <Feature: MAX(loans.rate)>, <Feature: MEAN(loans.loan_amount)>, <Feature: MEAN(loans.rate)>, <Feature: LAST(payments.missed)>, <Feature: LAST(payments.payment_amount)>, <Feature: LAST(payments.payment_id)>, <Feature: MAX(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount)>, <Feature: age / credit_score>, <Feature: age / income>, <Feature: credit_score / age>, <Feature: credit_score / income>, <Feature: income / age>, <Feature: income / credit_score>, <Feature: MONTH(joined)>, <Feature: age - credit_score>, <Feature: age - income>, <Feature: credit_score - income>, <Feature: YEAR(joined)>, <Feature: LAST(loans.MAX(payments.payment_amount))>, <Feature: LAST(loans.MEAN(payments.payment_amount))>, <Feature: LAST(loans.MONTH(loan_end))>, <Feature: LAST(loans.MONTH(loan_start))>, <Feature: LAST(loans.YEAR(loan_end))>, <Feature: LAST(loans.YEAR(loan_start))>, <Feature: LAST(loans.loan_amount - rate)>, <Feature: LAST(loans.loan_amount / rate)>, <Feature: LAST(loans.rate / loan_amount)>, <Feature: MAX(loans.LAST(payments.payment_amount))>, <Feature: MAX(loans.MEAN(payments.payment_amount))>, <Feature: MAX(loans.loan_amount - rate)>, <Feature: MAX(loans.loan_amount / rate)>, <Feature: MAX(loans.rate / loan_amount)>, <Feature: MEAN(loans.LAST(payments.payment_amount))>, <Feature: MEAN(loans.MAX(payments.payment_amount))>, <Feature: MEAN(loans.MEAN(payments.payment_amount))>, <Feature: MEAN(loans.loan_amount - rate)>, <Feature: MEAN(loans.loan_amount / rate)>, <Feature: MEAN(loans.rate / loan_amount)>, <Feature: LAST(payments.loans.client_id)>, <Feature: LAST(payments.loans.loan_amount)>, <Feature: LAST(payments.loans.loan_type)>, <Feature: LAST(payments.loans.rate)>, <Feature: LAST(payments.loans.repaid)>, <Feature: MAX(payments.loans.loan_amount)>, <Feature: MAX(payments.loans.rate)>, <Feature: MEAN(payments.loans.loan_amount)>, <Feature: MEAN(payments.loans.rate)>, <Feature: LAST(loans.loan_amount) / LAST(loans.rate)>, <Feature: LAST(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / MAX(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) / MAX(loans.rate)>, <Feature: LAST(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / MEAN(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) / MEAN(loans.rate)>, <Feature: LAST(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) / age>, <Feature: LAST(loans.loan_amount) / credit_score>, <Feature: LAST(loans.loan_amount) / income>, <Feature: LAST(loans.rate) / LAST(loans.loan_amount)>, <Feature: LAST(loans.rate) / LAST(payments.payment_amount)>, <Feature: LAST(loans.rate) / MAX(loans.loan_amount)>, <Feature: LAST(loans.rate) / MAX(loans.rate)>, <Feature: LAST(loans.rate) / MAX(payments.payment_amount)>, <Feature: LAST(loans.rate) / MEAN(loans.loan_amount)>, <Feature: LAST(loans.rate) / MEAN(loans.rate)>, <Feature: LAST(loans.rate) / MEAN(payments.payment_amount)>, <Feature: LAST(loans.rate) / age>, <Feature: LAST(loans.rate) / credit_score>, <Feature: LAST(loans.rate) / income>, <Feature: LAST(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / LAST(loans.rate)>, <Feature: LAST(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / MAX(loans.rate)>, <Feature: LAST(payments.payment_amount) / MAX(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) / MEAN(loans.rate)>, <Feature: LAST(payments.payment_amount) / MEAN(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) / age>, <Feature: LAST(payments.payment_amount) / credit_score>, <Feature: LAST(payments.payment_amount) / income>, <Feature: MAX(loans.loan_amount) / LAST(loans.loan_amount)>, <Feature: MAX(loans.loan_amount) / LAST(loans.rate)>, <Feature: MAX(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / MAX(loans.rate)>, <Feature: MAX(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / MEAN(loans.loan_amount)>, <Feature: MAX(loans.loan_amount) / MEAN(loans.rate)>, <Feature: MAX(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: MAX(loans.loan_amount) / age>, <Feature: MAX(loans.loan_amount) / credit_score>, <Feature: MAX(loans.loan_amount) / income>, <Feature: MAX(loans.rate) / LAST(loans.loan_amount)>, <Feature: MAX(loans.rate) / LAST(loans.rate)>, <Feature: MAX(loans.rate) / LAST(payments.payment_amount)>, <Feature: MAX(loans.rate) / MAX(loans.loan_amount)>, <Feature: MAX(loans.rate) / MAX(payments.payment_amount)>, <Feature: MAX(loans.rate) / MEAN(loans.loan_amount)>, <Feature: MAX(loans.rate) / MEAN(loans.rate)>, <Feature: MAX(loans.rate) / MEAN(payments.payment_amount)>, <Feature: MAX(loans.rate) / age>, <Feature: MAX(loans.rate) / credit_score>, <Feature: MAX(loans.rate) / income>, <Feature: MAX(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / LAST(loans.rate)>, <Feature: MAX(payments.payment_amount) / LAST(payments.payment_amount)>, <Feature: MAX(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / MAX(loans.rate)>, <Feature: MAX(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: MAX(payments.payment_amount) / MEAN(loans.rate)>, <Feature: MAX(payments.payment_amount) / MEAN(payments.payment_amount)>, <Feature: MAX(payments.payment_amount) / age>, <Feature: MAX(payments.payment_amount) / credit_score>, <Feature: MAX(payments.payment_amount) / income>, <Feature: MEAN(loans.loan_amount) / LAST(loans.loan_amount)>, <Feature: MEAN(loans.loan_amount) / LAST(loans.rate)>, <Feature: MEAN(loans.loan_amount) / LAST(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / MAX(loans.loan_amount)>, <Feature: MEAN(loans.loan_amount) / MAX(loans.rate)>, <Feature: MEAN(loans.loan_amount) / MAX(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / MEAN(loans.rate)>, <Feature: MEAN(loans.loan_amount) / MEAN(payments.payment_amount)>, <Feature: MEAN(loans.loan_amount) / age>, <Feature: MEAN(loans.loan_amount) / credit_score>, <Feature: MEAN(loans.loan_amount) / income>, <Feature: MEAN(loans.rate) / LAST(loans.loan_amount)>, <Feature: MEAN(loans.rate) / LAST(loans.rate)>, <Feature: MEAN(loans.rate) / LAST(payments.payment_amount)>, <Feature: MEAN(loans.rate) / MAX(loans.loan_amount)>, <Feature: MEAN(loans.rate) / MAX(loans.rate)>, <Feature: MEAN(loans.rate) / MAX(payments.payment_amount)>, <Feature: MEAN(loans.rate) / MEAN(loans.loan_amount)>, <Feature: MEAN(loans.rate) / MEAN(payments.payment_amount)>, <Feature: MEAN(loans.rate) / age>, <Feature: MEAN(loans.rate) / credit_score>, <Feature: MEAN(loans.rate) / income>, <Feature: MEAN(payments.payment_amount) / LAST(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / LAST(loans.rate)>, <Feature: MEAN(payments.payment_amount) / LAST(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount) / MAX(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / MAX(loans.rate)>, <Feature: MEAN(payments.payment_amount) / MAX(payments.payment_amount)>, <Feature: MEAN(payments.payment_amount) / MEAN(loans.loan_amount)>, <Feature: MEAN(payments.payment_amount) / MEAN(loans.rate)>, <Feature: MEAN(payments.payment_amount) / age>, <Feature: MEAN(payments.payment_amount) / credit_score>, <Feature: MEAN(payments.payment_amount) / income>, <Feature: age / LAST(loans.loan_amount)>, <Feature: age / LAST(loans.rate)>, <Feature: age / LAST(payments.payment_amount)>, <Feature: age / MAX(loans.loan_amount)>, <Feature: age / MAX(loans.rate)>, <Feature: age / MAX(payments.payment_amount)>, <Feature: age / MEAN(loans.loan_amount)>, <Feature: age / MEAN(loans.rate)>, <Feature: age / MEAN(payments.payment_amount)>, <Feature: credit_score / LAST(loans.loan_amount)>, <Feature: credit_score / LAST(loans.rate)>, <Feature: credit_score / LAST(payments.payment_amount)>, <Feature: credit_score / MAX(loans.loan_amount)>, <Feature: credit_score / MAX(loans.rate)>, <Feature: credit_score / MAX(payments.payment_amount)>, <Feature: credit_score / MEAN(loans.loan_amount)>, <Feature: credit_score / MEAN(loans.rate)>, <Feature: credit_score / MEAN(payments.payment_amount)>, <Feature: income / LAST(loans.loan_amount)>, <Feature: income / LAST(loans.rate)>, <Feature: income / LAST(payments.payment_amount)>, <Feature: income / MAX(loans.loan_amount)>, <Feature: income / MAX(loans.rate)>, <Feature: income / MAX(payments.payment_amount)>, <Feature: income / MEAN(loans.loan_amount)>, <Feature: income / MEAN(loans.rate)>, <Feature: income / MEAN(payments.payment_amount)>, <Feature: MONTH(LAST(loans.loan_end))>, <Feature: MONTH(LAST(loans.loan_start))>, <Feature: MONTH(LAST(payments.payment_date))>, <Feature: LAST(loans.loan_amount) - LAST(loans.rate)>, <Feature: LAST(loans.loan_amount) - LAST(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) - MAX(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) - MAX(loans.rate)>, <Feature: LAST(loans.loan_amount) - MAX(payments.payment_amount)>, <Feature: LAST(loans.loan_amount) - MEAN(loans.loan_amount)>, <Feature: LAST(loans.loan_amount) - MEAN(loans.rate)>, <Feature: LAST(loans.loan_amount) - MEAN(payments.payment_amount)>, <Feature: LAST(loans.rate) - LAST(payments.payment_amount)>, <Feature: LAST(loans.rate) - MAX(loans.loan_amount)>, <Feature: LAST(loans.rate) - MAX(loans.rate)>, <Feature: LAST(loans.rate) - MAX(payments.payment_amount)>, <Feature: LAST(loans.rate) - MEAN(loans.loan_amount)>, <Feature: LAST(loans.rate) - MEAN(loans.rate)>, <Feature: LAST(loans.rate) - MEAN(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) - MAX(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) - MAX(loans.rate)>, <Feature: LAST(payments.payment_amount) - MAX(payments.payment_amount)>, <Feature: LAST(payments.payment_amount) - MEAN(loans.loan_amount)>, <Feature: LAST(payments.payment_amount) - MEAN(loans.rate)>, <Feature: LAST(payments.payment_amount) - 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