Fix description for support parameters for sklearn patching algorithms
@@ -117,19 +117,19 @@ Intel(R) Extension for Scikit-learn patching affects performance of specific Sci
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|Classification|**SVC**|All parameters except `kernel` = 'poly' and 'sigmoid'. | No limitations.|
||**RandomForestClassifier**|All parameters except `warmstart` = True and `cpp_alpha` != 0, `criterion` != 'gini'. | Multi-output and sparse data is not supported. |
-||**KNeighborsClassifier**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` = 2. | Multi-output and sparse data is not supported. |
+||**KNeighborsClassifier**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` != 2. | Multi-output and sparse data is not supported. |
||**LogisticRegression / LogisticRegressionCV**|All parameters except `solver` != 'lbfgs' or 'newton-cg', `class_weight` != None, `sample_weight` != None. | Only dense data is supported. |
|Regression|**RandomForestRegressor**|All parameters except `warmstart` = True and `cpp_alpha` != 0, `criterion` != 'mse'. | Multi-output and sparse data is not supported. |
-||**KNeighborsRegressor**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` = 2. | Sparse data is not supported. |
+||**KNeighborsRegressor**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` != 2. | Sparse data is not supported. |
||**LinearRegression**|All parameters except `normalize` != False and `sample_weight` != None. | Only dense data is supported, `#observations` should be >= `#features`. |
||**Ridge**|All parameters except `normalize` != False, `solver` != 'auto' and `sample_weight` != None. | Only dense data is supported, `#observations` should be >= `#features`. |
||**ElasticNet**|All parameters except `sample_weight` != None. | Multi-output and sparse data is not supported, `#observations` should be >= `#features`. |
||**Lasso**|All parameters except `sample_weight` != None. | Multi-output and sparse data is not supported, `#observations` should be >= `#features`. |
|Clustering|**KMeans**|All parameters except `precompute_distances` and `sample_weight` != None. | No limitations. |
-||**DBSCAN**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` = 2. | Only dense data is supported. |
+||**DBSCAN**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` != 2, `algorithm` != `brute` or `auto` . | Only dense data is supported. |
|Dimensionality reduction|**PCA**|All parameters except `svd_solver` != 'full'. | No limitations. |
-|| **TSNE**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` = 2. | Sparse data is not supported. |
-|Unsupervised|**NearestNeighbors**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` = 2. | Sparse data is not supported. |
+|| **TSNE**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` != 2. | Sparse data is not supported. |
+|Unsupervised|**NearestNeighbors**|All parameters except `metric` != 'euclidean' or `minkowski` with `p` != 2. | Sparse data is not supported. |
|Other|**train_test_split**|All parameters are supported. | Only dense data is supported.|
||**assert_all_finite**|All parameters are supported. | Only dense data is supported. |
||**pairwise_distance**|With `metric`='cosine' and 'correlation'.| Only dense data is supported. |
