Description Usage Arguments Value Examples
Generates & plots the following performance evaluation & validation measures for Binary Classification Models - Hosmer Lemeshow goodness of fit tests, Calibration plots, Lift index & gain charts & concordance-discordance measures
1 2 | staticPerfMeasures(list_models, g, perf_measures = c("hosmer", "calibration",
"lift", "concord"), sample_size_concord = 5000)
|
list_models |
A list of one (or more) dataframes for each model whose performance is to be evaluated. Each dataframe should comprise of 2 columns with the first column indicating the class labels (0 or 1) and the second column providing the raw predicted probabilities |
g |
The number of groups for binning. The predicted probabilities are binned as follows For Hosmer-Lemshow (HL) test: Predicted probabilities binned as per g unique quantiles i.e. cut_points = unique(quantile(predicted_prob,seq(0,1,1/g))) For Lift-Index & Gain charts: Same as HL test, however if g > unique(predicted_probability), the predicted probabilities are used as such without binning For calibration plots, g equal sized intervals are created (of width 1/g each) |
perf_measures |
Select the required performance evaluation and validation measure/s, from the following options - c('hosmer','calibration','lift','concord'). Default option is All |
sample_size_concord |
For computing concordance-discordance measures (and c-statistic) a random sample is drawn from each dataset (if nrow(dataset) > 5000). Default sample size of 5000 can be adjusted by changing the value of this argument |
A nested list with 2 components - a list of dataframes and a list of plots - containing the outcomes of the different performance evaluations carried out.
1 2 3 4 5 |
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