Fully Automatic Generation of Scorecards

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Documentation for package ‘autoScorecard’ version 0.2.0

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auto_scorecard Functions to Automatically Generate Scorecards
best_iv Calculate the Best IV Value for the Binned Data
best_vs The Combination of Two Bins Produces the Best Binning Result
binning_eqfreq Equal Frequency Binning
binning_eqwid Equal Width Binning
binning_kmean The K-means Binning The k-means binning method first gives the center number, classifies the observation points using the Euclidean distance calculation and the distance from the center point, and then recalculates the center point until the center point no longer changes, and uses the classification result as the binning of the result.
bins_chim Chi-Square Binning Chi-square binning, using the ChiMerge algorithm for bottom-up merging based on the chi-square test.
bins_tree Automatic Binning Based on Decision Tree Automatic Binning Based on Decision Tree(rpart).
bins_unsupervised Unsupervised Automatic Binning Function By setting bin_nums, perform three unsupervised automatic binning
filter_var Data Filtering
get_IV Function to Calculate IV Value
noauto_scorecard Manually Input Parameters to Generate Scorecards
noauto_scorecard2 Manually Input Parameters to Generate Scorecards The basic score is dispersed into each feature score
plot_board Data Painter Function Draw K-S diagram, Lorenz diagram, lift diagram and AUC diagram.
psi_cal PSI Calculation Function
rep_woe Replace Feature Data by Binning Template