Pushing the docs for revision for branch: master, commit e2a2b4d403742c3eb2d0e085631a014b0975d6af

Author: sklearn-ci

dev/_downloads/hetero_feature_union.py

@@ -165,7 +165,7 @@ def transform(self, posts):
     ('svc', SVC(kernel='linear')),
 ])
 
-# limit the list of categories to make running this exmaple faster.
+# limit the list of categories to make running this example faster.
 categories = ['alt.atheism', 'talk.religion.misc']
 train = fetch_20newsgroups(random_state=1,
                            subset='train',

dev/_downloads/plot_ard.py

@@ -39,7 +39,7 @@
 relevant_features = np.random.randint(0, n_features, 10)
 for i in relevant_features:
     w[i] = stats.norm.rvs(loc=0, scale=1. / np.sqrt(lambda_))
-# Create noite with a precision alpha of 50.
+# Create noise with a precision alpha of 50.
 alpha_ = 50.
 noise = stats.norm.rvs(loc=0, scale=1. / np.sqrt(alpha_), size=n_samples)
 # Create the target

dev/_downloads/plot_calibration_multiclass.py

@@ -145,7 +145,7 @@ class of an instance (red: class 1, green: class 2, blue: class 3).
                         zip(calibrated_classifier.calibrators_, p.T)]).T
 prediction /= prediction.sum(axis=1)[:, None]
 
-# Ploit modifications of calibrator
+# Plot modifications of calibrator
 for i in range(prediction.shape[0]):
     plt.arrow(p[i, 0], p[i, 1],
               prediction[i, 0] - p[i, 0], prediction[i, 1] - p[i, 1],

dev/_downloads/plot_ensemble_oob.py

@@ -41,7 +41,7 @@
                            random_state=RANDOM_STATE)
 
 # NOTE: Setting the `warm_start` construction parameter to `True` disables
-# support for paralellised ensembles but is necessary for tracking the OOB
+# support for parallelized ensembles but is necessary for tracking the OOB
 # error trajectory during training.
 ensemble_clfs = [
     ("RandomForestClassifier, max_features='sqrt'",

dev/_downloads/plot_lasso_coordinate_descent_path.py

@@ -40,7 +40,7 @@
 alphas_enet, coefs_enet, _ = enet_path(
     X, y, eps=eps, l1_ratio=0.8, fit_intercept=False)
 
-print("Computing regularization path using the positve elastic net...")
+print("Computing regularization path using the positive elastic net...")
 alphas_positive_enet, coefs_positive_enet, _ = enet_path(
     X, y, eps=eps, l1_ratio=0.8, positive=True, fit_intercept=False)