Pushing the docs to dev/ for branch: main, commit ca0862a9dbc5dadab2ccd30828de6de0c6f1f69d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -80,7 +80,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Uncalibrated :class:`~sklearn.naive_bayes.GaussianNB` is poorly calibrated\nbecause of\nthe redundant features which violate the assumption of feature-independence\nand result in an overly confident classifier, which is indicated by the\ntypical transposed-sigmoid curve. Calibration of the probabilities of\n:class:`~sklearn.naive_bayes.GaussianNB` with `isotonic` can fix\nthis issue as can be seen from the nearly diagonal calibration curve.\n`Sigmoid regression <sigmoid_regressor>` also improves calibration\nslightly,\nalbeit not as strongly as the non-parametric isotonic regression. This can be\nattributed to the fact that we have plenty of calibration data such that the\ngreater flexibility of the non-parametric model can be exploited.\n\nBelow we will make a quantitative analysis considering several classification\nmetrics: `brier_score_loss`, `log_loss`,\n`precision, recall, F1 score <precision_recall_f_measure_metrics>` and\n`ROC AUC <roc_metrics>`.\n\n"
+        "Uncalibrated :class:`~sklearn.naive_bayes.GaussianNB` is poorly calibrated\nbecause of\nthe redundant features which violate the assumption of feature-independence\nand result in an overly confident classifier, which is indicated by the\ntypical transposed-sigmoid curve. Calibration of the probabilities of\n:class:`~sklearn.naive_bayes.GaussianNB` with `isotonic` can fix\nthis issue as can be seen from the nearly diagonal calibration curve.\n:ref:sigmoid regression `<sigmoid_regressor>` also improves calibration\nslightly,\nalbeit not as strongly as the non-parametric isotonic regression. This can be\nattributed to the fact that we have plenty of calibration data such that the\ngreater flexibility of the non-parametric model can be exploited.\n\nBelow we will make a quantitative analysis considering several classification\nmetrics: `brier_score_loss`, `log_loss`,\n`precision, recall, F1 score <precision_recall_f_measure_metrics>` and\n`ROC AUC <roc_metrics>`.\n\n"
       ]
     },
     {

dev/_downloads/6d4f620ec6653356eb970c2a6ed62081/plot_calibration_curve.ipynb

@@ -124,7 +124,7 @@
 # typical transposed-sigmoid curve. Calibration of the probabilities of
 # :class:`~sklearn.naive_bayes.GaussianNB` with :ref:`isotonic` can fix
 # this issue as can be seen from the nearly diagonal calibration curve.
-# :ref:`Sigmoid regression <sigmoid_regressor>` also improves calibration
+# :ref:sigmoid regression `<sigmoid_regressor>` also improves calibration
 # slightly,
 # albeit not as strongly as the non-parametric isotonic regression. This can be
 # attributed to the fact that we have plenty of calibration data such that the

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -41,7 +41,7 @@
 plt.subplot(4, 1, 1)
 plt.xlim(0, 512)
 plt.title("Sparse signal")
-plt.stem(idx, w[idx])
+plt.stem(idx, w[idx], use_line_collection=True)
 
 # plot the noise-free reconstruction
 omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
@@ -51,7 +51,7 @@
 plt.subplot(4, 1, 2)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noise-free measurements")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 # plot the noisy reconstruction
 omp.fit(X, y_noisy)
@@ -60,7 +60,7 @@
 plt.subplot(4, 1, 3)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 # plot the noisy reconstruction with number of non-zeros set by CV
 omp_cv = OrthogonalMatchingPursuitCV()
@@ -70,7 +70,7 @@
 plt.subplot(4, 1, 4)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements with CV")
-plt.stem(idx_r, coef[idx_r])
+plt.stem(idx_r, coef[idx_r], use_line_collection=True)
 
 plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
 plt.suptitle("Sparse signal recovery with Orthogonal Matching Pursuit", fontsize=16)