add_white_noise |
Target-encoding methods |
auc_score |
Area Under the Receiver Operating Characteristic |
collinear |
Automated multicollinearity management |
cor_df |
Correlation data frame of numeric and character variables |
cor_matrix |
Correlation matrix of numeric and character variables |
cor_select |
Automated multicollinearity reduction via pairwise correlation |
cramer_v |
Bias Corrected Cramer's V |
f_gam_auc_balanced |
AUC of Logistic GAM Model |
f_gam_auc_unbalanced |
AUC of Logistic GAM Model with Weighted Cases |
f_gam_deviance |
Explained Deviance from univariate GAM model |
f_logistic_auc_balanced |
AUC of Binomial GLM with Logit Link |
f_logistic_auc_unbalanced |
AUC of Binomial GLM with Logit Link and Case Weights |
f_rf_auc_balanced |
AUC of Random Forest model of a balanced binary response |
f_rf_auc_unbalanced |
AUC of Random Forest model of an unbalanced binary response |
f_rf_deviance |
R-squared of Random Forest model |
f_rf_rsquared |
R-squared of Random Forest model |
f_rsquared |
R-squared between a response and a predictor |
identify_non_numeric_predictors |
Identify non-numeric predictors |
identify_numeric_predictors |
Identify numeric predictors |
identify_zero_variance_predictors |
Identify zero and near-zero-variance predictors |
preference_order |
Compute the preference order for predictors based on a user-defined function. |
target_encoding_lab |
Target encoding of non-numeric variables |
target_encoding_loo |
Target-encoding methods |
target_encoding_mean |
Target-encoding methods |
target_encoding_rank |
Target-encoding methods |
target_encoding_rnorm |
Target-encoding methods |
toy |
One response and four predictors with varying levels of multicollinearity |
validate_df |
Validate input data frame |
validate_predictors |
Validate the 'predictors' argument for analysis |
validate_response |
Validate the 'response' argument for target encoding of non-numeric variables |
vi |
30.000 records of responses and predictors all over the world |
vif_df |
Variance Inflation Factor |
vif_select |
Automated multicollinearity reduction via Variance Inflation Factor |
vi_predictors |
Predictor names in data frame 'vi' |