|
add_woe()
|
Add WoE in a data frame |
|
dictionary()
|
Weight of evidence dictionary |
|
is_tf_available()
|
Test to see if tensorflow is available |
|
step_discretize_cart() tidy(<step_discretize_cart>)
|
Discretize numeric variables with CART |
|
step_discretize_xgb() tidy(<step_discretize_xgb>)
|
Discretize numeric variables with XgBoost |
|
step_embed() tidy(<step_embed>) embed_control()
|
Encoding Factors into Multiple Columns |
|
step_feature_hash() tidy(<step_feature_hash>)
|
Dummy Variables Creation via Feature Hashing |
|
step_lencode_bayes() tidy(<step_lencode_bayes>)
|
Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings |
|
step_lencode_glm() tidy(<step_lencode_glm>)
|
Supervised Factor Conversions into Linear Functions using Likelihood Encodings |
|
step_lencode_mixed() tidy(<step_lencode_mixed>)
|
Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings |
|
step_umap() tidy(<step_umap>)
|
Supervised and unsupervised uniform manifold approximation and projection (UMAP) |
|
step_woe() tidy(<step_woe>)
|
Weight of evidence transformation |
|
woe_table()
|
Crosstable with woe between a binary outcome and a predictor variable. |