This function lets the user get a confusion matrix and accuracy, and for for binary classification models: AUC, Precision, Sensitivity, and Specificity.
model_metrics(tag, score, multis = NA, abc = TRUE, thresh = 10,
thresh_cm = 0.5, plots = TRUE, subtitle = NA)Vector. Real known label
Vector. Predicted value or model's result
Data.frame. Containing columns with each category score (only used when more than 2 categories coexist)
Boolean. Arrange columns and rows alphabetically when categorical values?
Integer. Threshold for selecting binary or regression models: this number is the threshold of unique values we should have in 'tag' (more than: regression; less than: classification)
Numeric. Value to splits the results for the confusion matrix. Range of values: (0-1)
Boolean. Include plots?
Character. Subtitle for plots
Other Machine Learning: ROC,
clusterKmeans, conf_mat,
export_results, gain_lift,
h2o_automl, h2o_predict_API,
h2o_predict_MOJO,
h2o_predict_binary,
h2o_predict_model,
h2o_selectmodel, impute,
iter_seeds, mplot_conf,
mplot_cuts_error, mplot_cuts,
mplot_density, mplot_full,
mplot_gain, mplot_importance,
mplot_lineal, mplot_metrics,
mplot_response, mplot_roc,
mplot_splits, msplit
Other Calculus: ROC, conf_mat,
corr, deg2num,
dist2d, errors,
loglossBinary, mae,
mape, mse,
quants, rmse,
rsqa, rsq