expanded doc/sklearn.rst
@@ -9,20 +9,24 @@ provided by daal4py allows one to create scikit-learn compatible estimators,
transformers, clusterers, etc. powered by DAAL which are nearly as efficient as
native programs.
-Submodule daal4py.sklearn provides such classes.
+Submodule `daal4py.sklearn` provides such classes.
Currently, these include::
1. daal4py.sklearn.neighbors.KNeighborsClassifier
2. daal4py.sklearn.ensemble.RandomForestClassifier
3. daal4py.sklearn.ensemble.RandomForestRegressor
-
+ 4. daal4py.sklearn.cluster.KMeans
+ 5. daal4py.sklearn.decomposition.PCA
+ 6. daal4py.sklearn.linear_model.Ridge
+ 7. daal4py.sklearn.svm.SVC
+ 8. daal4py.sklearn.linear_model.logistic_regression_path
Additionally, daal4py contains code to monkey-patch the following existing scikit-learn algorithms:
1. `sklearn.linear_model.LinearRegression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html>`__
2. `sklearn.linear_model.Ridge <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html>`__ (solver='auto')
-3. `sklearn.linear_model.LogisticRegression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ (solver='lbfgs')
+3. `sklearn.linear_model.LogisticRegression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ and `sklearn.linear_model.LogisticRegressionCV <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html>`__ (solver in ['lbfgs', 'newton-cg'])
4. `sklearn.decomposition.PCA <https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html>`__ (solver='full')
5. `sklearn.cluster.KMeans <https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html>`__ (algo='full')
6. `sklearn.metric.pairwise_distance <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html>`__, with metric='cosine' or metric='correlation'
@@ -30,4 +34,4 @@ Additionally, daal4py contains code to monkey-patch the following existing sciki
Monkey-patched scikit-learn passes scikit-learn's own test suite, with few exceptions, specified in `deselected_tests.yaml <https://github.com/IntelPython/daal4py/blob/master/deselected_tests.yaml>`__.
-
+Scikit-learn's test suite executes `check_estimator <https://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.check_estimator.html>`__ on all added and monkey-patched classes, which are discovered by means of introspection. This assures scikit-learn API compatibility of `daal4py.sklearn` classes.
