Kolmogorov-Smirnov (KS) statistics is used to assess predictive power for marketing or credit risk models. It is the maximum difference between cumulative event and non-event distribution across score/probability bands. The gains table typically has across score bands and can be used to find the KS for a model.

blr_ks_chart(gains_table, title = "KS Chart", yaxis_title = " ", xaxis_title = "Cumulative Population %", ks_line_color = "black")

gains_table | An object of class |
---|---|

title | Plot title. |

yaxis_title | Y axis title. |

xaxis_title | X axis title. |

ks_line_color | Color of the line indicating maximum KS statistic. |

Tjur, T. (2009), "Coefficients of Determination in Logistic Regression Models — A New Proposal: The Coefficient of Discrimination," The American Statistician, 63(4), 366-372.

Horn, S. D. (1977), Goodness-of-fit tests for discrete data: a review and an application to a health impairment scale, Biometrics, 33, 237–247.

Other model validation techniques: `blr_confusion_matrix`

,
`blr_decile_capture_rate`

,
`blr_decile_lift_chart`

,
`blr_gains_table`

,
`blr_gini_index`

,
`blr_lorenz_curve`

,
`blr_roc_curve`

,
`blr_test_hosmer_lemeshow`

model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) blr_ks_chart(gt)