Pushing the docs to dev/ for branch: master, commit 06e9a3fe39e0f5bcd46c4b8a65323a53286f8374

Author: sklearn-ci

dev/_downloads/0805c1058fba7383113f44df060e3b88/plot_changed_only_pprint_parameter.py

@@ -5,7 +5,7 @@
 
 This example illustrates the use of the print_changed_only global parameter.
 
-Setting print_changed_only to True will alterate the representation of
+Setting print_changed_only to True will alternate the representation of
 estimators to only show the parameters that have been set to non-default
 values. This can be used to have more compact representations.
 """

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -151,7 +151,7 @@
 # .........................................
 # The :func:`sklearn.metrics.roc_auc_score` function can be used for
 # multi-class classification. The multi-class One-vs-One scheme compares every
-# unique pairwise combination of classes. In this section, we calcuate the AUC
+# unique pairwise combination of classes. In this section, we calculate the AUC
 # using the OvR and OvO schemes. We report a macro average, and a
 # prevalence-weighted average.
 y_prob = classifier.predict_proba(X_test)

dev/_downloads/60b2a2c4441794028ceb207b0614746f/plot_roc.py

@@ -6,7 +6,7 @@
 An illustration of t-SNE on the two concentric circles and the S-curve
 datasets for different perplexity values.
 
-We observe a tendency towards clearer shapes as the preplexity value increases.
+We observe a tendency towards clearer shapes as the perplexity value increases.
 
 The size, the distance and the shape of clusters may vary upon initialization,
 perplexity values and does not always convey a meaning.

dev/_downloads/6530c9428bacd72600c7516d4bc1b117/plot_t_sne_perplexity.py

@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Area under ROC for the multiclass problem\n.........................................\nThe :func:`sklearn.metrics.roc_auc_score` function can be used for\nmulti-class classification. The multi-class One-vs-One scheme compares every\nunique pairwise combination of classes. In this section, we calcuate the AUC\nusing the OvR and OvO schemes. We report a macro average, and a\nprevalence-weighted average.\n\n"
+        "Area under ROC for the multiclass problem\n.........................................\nThe :func:`sklearn.metrics.roc_auc_score` function can be used for\nmulti-class classification. The multi-class One-vs-One scheme compares every\nunique pairwise combination of classes. In this section, we calculate the AUC\nusing the OvR and OvO schemes. We report a macro average, and a\nprevalence-weighted average.\n\n"
       ]
     },
     {

dev/_downloads/830cf55f85ef397d9f388916c4de43e9/plot_roc.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Compact estimator representations\n\n\nThis example illustrates the use of the print_changed_only global parameter.\n\nSetting print_changed_only to True will alterate the representation of\nestimators to only show the parameters that have been set to non-default\nvalues. This can be used to have more compact representations.\n"
+        "\n# Compact estimator representations\n\n\nThis example illustrates the use of the print_changed_only global parameter.\n\nSetting print_changed_only to True will alternate the representation of\nestimators to only show the parameters that have been set to non-default\nvalues. This can be used to have more compact representations.\n"
       ]
     },
     {

dev/_downloads/8407971cce85862f629c5e657b6e6e98/plot_changed_only_pprint_parameter.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n=============================================================================\nt-SNE: The effect of various perplexity values on the shape\n=============================================================================\n\nAn illustration of t-SNE on the two concentric circles and the S-curve\ndatasets for different perplexity values.\n\nWe observe a tendency towards clearer shapes as the preplexity value increases.\n\nThe size, the distance and the shape of clusters may vary upon initialization,\nperplexity values and does not always convey a meaning.\n\nAs shown below, t-SNE for higher perplexities finds meaningful topology of\ntwo concentric circles, however the size and the distance of the circles varies\nslightly from the original. Contrary to the two circles dataset, the shapes\nvisually diverge from S-curve topology on the S-curve dataset even for\nlarger perplexity values.\n\nFor further details, \"How to Use t-SNE Effectively\"\nhttps://distill.pub/2016/misread-tsne/ provides a good discussion of the\neffects of various parameters, as well as interactive plots to explore\nthose effects.\n"
+        "\n=============================================================================\nt-SNE: The effect of various perplexity values on the shape\n=============================================================================\n\nAn illustration of t-SNE on the two concentric circles and the S-curve\ndatasets for different perplexity values.\n\nWe observe a tendency towards clearer shapes as the perplexity value increases.\n\nThe size, the distance and the shape of clusters may vary upon initialization,\nperplexity values and does not always convey a meaning.\n\nAs shown below, t-SNE for higher perplexities finds meaningful topology of\ntwo concentric circles, however the size and the distance of the circles varies\nslightly from the original. Contrary to the two circles dataset, the shapes\nvisually diverge from S-curve topology on the S-curve dataset even for\nlarger perplexity values.\n\nFor further details, \"How to Use t-SNE Effectively\"\nhttps://distill.pub/2016/misread-tsne/ provides a good discussion of the\neffects of various parameters, as well as interactive plots to explore\nthose effects.\n"
       ]
     },
     {

dev/_downloads/b6cd0a64268e81592d4c6c637220108c/plot_t_sne_perplexity.ipynb

@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Training a Random Forest and Plotting the ROC Curve\n--------------------------------------------------------\nWe train a random forest classifier and create a plot comparing it to the SVC\nROC curve. Notice how `svc_disp` uses\n:func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve\nwithout recomputing the values of the roc curve itself. Futhermore, we\npass `alpha=0.8` to the plot functions to adjust the alpha values of the\ncurves.\n\n"
+        "Training a Random Forest and Plotting the ROC Curve\n--------------------------------------------------------\nWe train a random forest classifier and create a plot comparing it to the SVC\nROC curve. Notice how `svc_disp` uses\n:func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve\nwithout recomputing the values of the roc curve itself. Furthermore, we\npass `alpha=0.8` to the plot functions to adjust the alpha values of the\ncurves.\n\n"
       ]
     },
     {

dev/_downloads/c60067c77f8feac9913d498602b7257e/plot_roc_curve_visualization_api.ipynb

@@ -44,7 +44,7 @@
 # We train a random forest classifier and create a plot comparing it to the SVC
 # ROC curve. Notice how `svc_disp` uses
 # :func:`~sklearn.metrics.RocCurveDisplay.plot` to plot the SVC ROC curve
-# without recomputing the values of the roc curve itself. Futhermore, we
+# without recomputing the values of the roc curve itself. Furthermore, we
 # pass `alpha=0.8` to the plot functions to adjust the alpha values of the
 # curves.
 rfc = RandomForestClassifier(n_estimators=10, random_state=42)