Update documentation

category_encoders/cat_boost.py

@@ -12,6 +12,8 @@
 class CatBoostEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """CatBoost coding for categorical features.
 
+    Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
+
     This is very similar to leave-one-out encoding, but calculates the
     values "on-the-fly". Consequently, the values naturally vary
     during the training phase and it is not necessary to add random noise.

category_encoders/glmm.py

@@ -16,6 +16,8 @@
 class GLMMEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """Generalized linear mixed model.
 
+    Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
+
     This is a supervised encoder similar to TargetEncoder or MEstimateEncoder, but there are some advantages:
     1) Solid statistical theory behind the technique. Mixed effects models are a mature branch of statistics.
     2) No hyper-parameters to tune. The amount of shrinkage is automatically determined through the estimation process.

category_encoders/james_stein.py

@@ -14,6 +14,8 @@
 class JamesSteinEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """James-Stein estimator.
 
+    Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
+
     For feature value `i`, James-Stein estimator returns a weighted average of:
 
         1. The mean target value for the observed feature value `i`.

category_encoders/m_estimate.py

@@ -12,6 +12,8 @@
 class MEstimateEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """M-probability estimate of likelihood.
 
+    Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
+
     This is a simplified version of target encoder, which goes under names like m-probability estimate or
     additive smoothing with known incidence rates. In comparison to target encoder, m-probability estimate
     has only one tunable parameter (`m`), while target encoder has two tunable parameters (`min_samples_leaf`

category_encoders/target_encoder.py

@@ -11,6 +11,8 @@
 class TargetEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """Target encoding for categorical features.
 
+    Supported targets: binomial and continuous. For polynomial target support, see PolynomialWrapper.
+
     For the case of categorical target: features are replaced with a blend of posterior probability of the target
     given particular categorical value and the prior probability of the target over all the training data.
 

category_encoders/woe.py

@@ -12,6 +12,8 @@
 class WOEEncoder(BaseEstimator, util.TransformerWithTargetMixin):
     """Weight of Evidence coding for categorical features.
 
+    Supported targets: binomial. For polynomial target support, see PolynomialWrapper.
+
     Parameters
     ----------