continue

Author: Pahandrovich

README.md

@@ -6,7 +6,9 @@
 **Machine Learning Benchmarks** contains implementations of machine learning algorithms
 across data analytics frameworks.  Scikit-learn_bench can be extended to add new frameworks
 and algorithms. It currently support the [scikit-learn](https://scikit-learn.org/),
-[DAAL4PY](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py), [cuML](https://github.com/rapidsai/cuml),
+[DAAL4PY](https://github.com/intel/scikit-learn-intelex/tree/master/daal4py),
+[Intel(R) Extension for Scikit-learn](https://github.com/intel/scikit-learn-intelex),
+[cuML](https://github.com/rapidsai/cuml),
 and [XGBoost](https://github.com/dmlc/xgboost) frameworks for commonly used
 [machine learning algorithms](#supported-algorithms).
 
@@ -92,7 +94,7 @@ The configuration of benchmarks allows you to select the frameworks to run, sele
 
 ## Benchmark supported algorithms
 
-| algorithm  | benchmark name | sklearn | daal4py | cuml | xgboost |
+| algorithm  | benchmark name | sklearn | daal4py/sklearnex | cuml | xgboost |
 |---|---|---|---|---|---|
 |**[DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html)**|dbscan|:white_check_mark:|:white_check_mark:|:white_check_mark:|:x:|
 |**[RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)**|df_clfs|:white_check_mark:|:white_check_mark:|:white_check_mark:|:x:|