Pushing the docs to dev/ for branch: master, commit f4e7d2b19a9432f66ed22b01bae76f31af1db00f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Compare the effect of different scalers on data with outliers\n\n\nFeature 0 (median income in a block) and feature 5 (number of households) of\nthe `California housing dataset\n<http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html>`_ have very\ndifferent scales and contain some very large outliers. These two\ncharacteristics lead to difficulties to visualize the data and, more\nimportantly, they can degrade the predictive performance of many machine\nlearning algorithms. Unscaled data can also slow down or even prevent the\nconvergence of many gradient-based estimators.\n\nIndeed many estimators are designed with the assumption that each feature takes\nvalues close to zero or more importantly that all features vary on comparable\nscales. In particular, metric-based and gradient-based estimators often assume\napproximately standardized data (centered features with unit variances). A\nnotable exception are decision tree-based estimators that are robust to\narbitrary scaling of the data.\n\nThis example uses different scalers, transformers, and normalizers to bring the\ndata within a pre-defined range.\n\nScalers are linear (or more precisely affine) transformers and differ from each\nother in the way to estimate the parameters used to shift and scale each\nfeature.\n\n``QuantileTransformer`` provides non-linear transformations in which distances\nbetween marginal outliers and inliers are shrunk. ``PowerTransformer`` provides\nnon-linear transformations in which data is mapped to a normal distribution to\nstabilize variance and minimize skewness.\n\nUnlike the previous transformations, normalization refers to a per sample\ntransformation instead of a per feature transformation.\n\nThe following code is a bit verbose, feel free to jump directly to the analysis\nof the results_.\n\n\n"
+        "\n# Compare the effect of different scalers on data with outliers\n\n\nFeature 0 (median income in a block) and feature 5 (number of households) of\nthe `California housing dataset\n<https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html>`_ have very\ndifferent scales and contain some very large outliers. These two\ncharacteristics lead to difficulties to visualize the data and, more\nimportantly, they can degrade the predictive performance of many machine\nlearning algorithms. Unscaled data can also slow down or even prevent the\nconvergence of many gradient-based estimators.\n\nIndeed many estimators are designed with the assumption that each feature takes\nvalues close to zero or more importantly that all features vary on comparable\nscales. In particular, metric-based and gradient-based estimators often assume\napproximately standardized data (centered features with unit variances). A\nnotable exception are decision tree-based estimators that are robust to\narbitrary scaling of the data.\n\nThis example uses different scalers, transformers, and normalizers to bring the\ndata within a pre-defined range.\n\nScalers are linear (or more precisely affine) transformers and differ from each\nother in the way to estimate the parameters used to shift and scale each\nfeature.\n\n``QuantileTransformer`` provides non-linear transformations in which distances\nbetween marginal outliers and inliers are shrunk. ``PowerTransformer`` provides\nnon-linear transformations in which data is mapped to a normal distribution to\nstabilize variance and minimize skewness.\n\nUnlike the previous transformations, normalization refers to a per sample\ntransformation instead of a per feature transformation.\n\nThe following code is a bit verbose, feel free to jump directly to the analysis\nof the results_.\n\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -8,7 +8,7 @@
 
 Feature 0 (median income in a block) and feature 5 (number of households) of
 the `California housing dataset
-<http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html>`_ have very
+<https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html>`_ have very
 different scales and contain some very large outliers. These two
 characteristics lead to difficulties to visualize the data and, more
 importantly, they can degrade the predictive performance of many machine

dev/_downloads/plot_all_scaling.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# The Digit Dataset\n\n\nThis dataset is made up of 1797 8x8 images. Each image,\nlike the one shown below, is of a hand-written digit.\nIn order to utilize an 8x8 figure like this, we'd have to\nfirst transform it into a feature vector with length 64.\n\nSee `here\n<http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_\nfor more information about this dataset.\n\n"
+        "\n# The Digit Dataset\n\n\nThis dataset is made up of 1797 8x8 images. Each image,\nlike the one shown below, is of a hand-written digit.\nIn order to utilize an 8x8 figure like this, we'd have to\nfirst transform it into a feature vector with length 64.\n\nSee `here\n<https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_\nfor more information about this dataset.\n\n"
       ]
     },
     {

dev/_downloads/plot_all_scaling.py

@@ -12,7 +12,7 @@
 first transform it into a feature vector with length 64.
 
 See `here
-<http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_
+<https://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits>`_
 for more information about this dataset.
 """
 print(__doc__)

dev/_downloads/plot_digits_last_image.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\"\nhttp://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"
+        "\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"
       ]
     },
     {

dev/_downloads/plot_digits_last_image.py

@@ -18,7 +18,7 @@
 larger perplexity values.
 
 For further details, "How to Use t-SNE Effectively"
-http://distill.pub/2016/misread-tsne/ provides a good discussion of the
+https://distill.pub/2016/misread-tsne/ provides a good discussion of the
 effects of various parameters, as well as interactive plots to explore
 those effects.
 """