DOC 0.24.0 release highlights formatting
@@ -146,7 +146,7 @@ knn = KNeighborsClassifier(n_neighbors=3)
sfs = SequentialFeatureSelector(knn, n_features_to_select=2)
sfs.fit(X, y)
print("Features selected by forward sequential selection: "
- f"{feature_names[sfs.get_support().tolist()]}")
+ f"{feature_names[sfs.get_support()].tolist()}")
##############################################################################
# New PolynomialCountSketch kernel approximation function
@@ -172,8 +172,8 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=5000,
random_state=42)
pipe.fit(X_train, y_train).score(X_test, y_test)
-# ##############################################################################
-# # For comparison, here is the score of a linear baseline for the same data:
+##############################################################################
+# For comparison, here is the score of a linear baseline for the same data:
linear_baseline = make_pipeline(MinMaxScaler(),
LogisticRegression(max_iter=1000))
