features.md

Helpers:
- sklearn2pmml.EstimatorProxy
- sklearn2pmml.SelectorProxy
- sklearn2pmml.h2o.H2OEstimatorProxy
Feature cross-references:
- sklearn2pmml.cross_reference.Memorizer
- sklearn2pmml.cross_reference.Recaller
Feature specification and decoration:
- sklearn2pmml.decoration.Alias
- sklearn2pmml.decoration.CategoricalDomain
- sklearn2pmml.decoration.ContinuousDomain
- sklearn2pmml.decoration.ContinuousDomainEraser
- sklearn2pmml.decoration.DateDomain
- sklearn2pmml.decoration.DateTimeDomain
- sklearn2pmml.decoration.DiscreteDomainEraser
- sklearn2pmml.decoration.MultiAlias
- sklearn2pmml.decoration.MultiDomain
- sklearn2pmml.decoration.OrdinalDomain
Ensemble methods:
- sklearn2pmml.ensemble.EstimatorChain
- sklearn2pmml.ensemble.GBDTLMRegressor
  - The GBDT side: All Scikit-Learn decision tree ensemble regressors, LGBMRegressor, XGBRegressor, XGBRFRegressor.
  - The LM side: A Scikit-Learn linear regressor (eg. ElasticNet, LinearRegression, SGDRegressor).
- sklearn2pmml.ensemble.GBDTLRClassifier
  - The GBDT side: All Scikit-Learn decision tree ensemble classifiers, LGBMClassifier, XGBClassifier, XGBRFClassifier.
  - The LR side: A Scikit-Learn binary linear classifier (eg. LinearSVC, LogisticRegression, SGDClassifier).
- sklearn2pmml.ensemble.SelectFirstClassifier
- sklearn2pmml.ensemble.SelectFirstRegressor
UDF models:
- sklearn2pmml.expression.ExpressionClassifier
- sklearn2pmml.expression.ExpressionRegressor
Feature selection:
- sklearn2pmml.feature_selection.SelectUnique
Linear models:
- sklearn2pmml.statsmodels.StatsModelsClassifier
- sklearn2pmml.statsmodels.StatsModelsOrdinalClassifier
- sklearn2pmml.statsmodels.StatsModelsRegressor
Neural networks:
- sklearn2pmml.neural_network.MLPTransformer
Pipeline:
- sklearn2pmml.pipeline.PMMLPipeline
Postprocessing:
- sklearn2pmml.postprocessing.BusinessDecisionTransformer
- sklearn2pmml.postprocessing.FeatureExporter
Preprocessing:
- sklearn2pmml.preprocessing.AggregateTransformer
- sklearn2pmml.preprocessing.BSplineTransformer
- sklearn2pmml.preprocessing.CastTransformer
- sklearn2pmml.preprocessing.ConcatTransformer
- sklearn2pmml.preprocessing.CutTransformer
- sklearn2pmml.preprocessing.DataFrameConstructor
- sklearn2pmml.preprocessing.DateTimeFormatter
- sklearn2pmml.preprocessing.DaysSinceYearTransformer
- sklearn2pmml.preprocessing.ExpressionTransformer
  - Ternary conditional expression <expression_true> if <condition> else <expression_false>.
  - Array indexing expressions X[<column index>] and X[<column name>].
  - String concatenation expressions.
  - String slicing expressions <str>[<start>:<stop>].
  - Arithmetic operators +, -, *, /, // and %.
  - The power operator **.
  - Identity comparison operators is None and is not None.
  - Comparison operators in <list>, not in <list>, <=, <, ==, !=, > and >=.
  - Logical operators and, or and not.
  - Built-in functions (too numerous to list).
  - Built-in type cast functions bool, float, int and str.
  - Math constants math.e, math.nan, math.pi and math.tau.
  - Math functions (too numerous to list).
  - Numpy constants numpy.e, numpy.NaN. numpy.NZERO, numpy.pi and numpy.PZERO.
  - Numpy function numpy.where.
  - Numpy universal functions (too numerous to list).
  - Pandas constants pandas.NA and pandas.NaT.
  - Pandas functions pandas.isna, pandas.isnull, pandas.notna and pandas.notnull.
  - Scipy functions scipy.special.expit and scipy.special.logit.
  - String functions startswith(<prefix>), endswith(<suffix>), lower, upper and strip.
  - String length function len(<str>).
  - Perl Compatible Regular Expression (PCRE) functions pcre.search and pcre.sub.
  - Regular Expression (RE) functions re.search, and re.sub.
  - User-defined functions.
- sklearn2pmml.preprocessing.FilterLookupTransformer
- sklearn2pmml.preprocessing.IdentityTransformer
- sklearn2pmml.preprocessing.LagTransformer
- sklearn2pmml.preprocessing.LookupTransformer
- sklearn2pmml.preprocessing.MatchesTransformer
- sklearn2pmml.preprocessing.MultiCastTransformer
- sklearn2pmml.preprocessing.MultiLookupTransformer
- sklearn2pmml.preprocessing.NumberFormatter
- sklearn2pmml.preprocessing.PMMLLabelBinarizer
- sklearn2pmml.preprocessing.PMMLLabelEncoder
- sklearn2pmml.preprocessing.PowerFunctionTransformer
- sklearn2pmml.preprocessing.ReplaceTransformer
- sklearn2pmml.preprocessing.RollingAggregateTransformer
- sklearn2pmml.preprocessing.SecondsSinceMidnightTransformer
- sklearn2pmml.preprocessing.SecondsSinceYearTransformer
- sklearn2pmml.preprocessing.SelectFirstTransformer
- sklearn2pmml.preprocessing.SeriesConstructor
- sklearn2pmml.preprocessing.StringLengthTransformer
- sklearn2pmml.preprocessing.StringNormalizer
- sklearn2pmml.preprocessing.SubstringTransformer
- sklearn2pmml.preprocessing.WordCountTransformer
- sklearn2pmml.preprocessing.h2o.H2OFrameConstructor
- sklearn2pmml.util.Reshaper
- sklearn2pmml.util.Slicer
Rule sets:
- sklearn2pmml.ruleset.RuleSetClassifier
Decision trees:
- sklearn2pmml.tree.chaid.CHAIDClassifier
- sklearn2pmml.tree.chaid.CHAIDRegressor

Sklearn-Pandas

Examples: main.py

sklearn_pandas.CategoricalImputer
sklearn_pandas.DataFrameMapper

StatsModels

Examples: main-statsmodels.py

TPOT

Examples: extensions/tpot.py

tpot.builtins.stacking_estimator.StackingEstimator

Treeple (formerly Scikit-Tree)

Examples: extensions/treeple.py

XGBoost

Examples: main-xgboost.py, extensions/category_encoders-xgboost.py and extensions/categorical.py