diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 7ec175883d4cd..14a7bc9f7e076 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -48,6 +48,8 @@ columns: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_coclustering_003.png :align: center :scale: 50 + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The darkest 5 + squares of heat map run diagonally from top left to bottom right. An example of biclusters formed by partitioning rows and columns. @@ -60,6 +62,8 @@ small: :target: ../auto_examples/bicluster/images/sphx_glr_plot_spectral_biclustering_003.png :align: center :scale: 50 + :alt: The graph is a square heat map, 5x5, with axes from 0 to 250. The variance of + the values within each bicluster is small, causing a checkerboard effect. An example of checkerboard biclusters. diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index 1fcd1d501d100..096350c7fa85b 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -53,6 +53,10 @@ by showing the number of samples in each predicted probability bin. .. figure:: ../auto_examples/calibration/images/sphx_glr_plot_compare_calibration_001.png :target: ../auto_examples/calibration/plot_compare_calibration.html :align: center + :alt: Five plots comparing calibration classifiers. The first plot compares + Logistic, Naive Bayes, SVC, and Random forest classifiers side by side with perfect + calibration. The following four histograms isolate each classifier to describe + their predicted probability on the x-axis and count on the y-axis. .. currentmodule:: sklearn.linear_model diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 65f33fe1fbebb..0cca7a3c16219 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -272,6 +272,10 @@ small, as shown in the example and cited reference. :target: ../auto_examples/cluster/plot_mini_batch_kmeans.html :align: center :scale: 100 + :alt: A figure of three panels with scatter plots for KMeans, MiniBatchKMeans, + and their difference. Both methods have very similar results, the left and + middle panels show the three identified clusters. The difference + panel highlights the <20 points that differ out of the 3000 points. .. topic:: Examples: @@ -310,6 +314,7 @@ is given. :target: ../auto_examples/cluster/plot_affinity_propagation.html :align: center :scale: 50 + :alt: Three distinct clusters created using affinity propagation. Affinity Propagation can be interesting as it chooses the number of @@ -461,10 +466,15 @@ computed using a function of a gradient of the image. .. |noisy_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_001.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 + :alt: An image of connected nearly equally sized circles. The circles are in + shades of green, blue, and yellow. The entire image is pixelated, blurry. .. |segmented_img| image:: ../auto_examples/cluster/images/sphx_glr_plot_segmentation_toy_002.png :target: ../auto_examples/cluster/plot_segmentation_toy.html :scale: 50 + :alt: An image of connected nearly equally sized circles. Each circle has a + distinct color: dark green, light green, blue, and yellow. The clarity of the image + is much better compared to the previous one. .. centered:: |noisy_img| |segmented_img| @@ -641,10 +651,20 @@ the roll. .. |unstructured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_001.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 + :alt: A 3D scatter plot of 1500 data points. Points are located along a swirl + or swiss roll. The six clusters are highlighted in different colours. + The hierarchical clustering is performed without connectivity + constraints on the structure and is solely based on distance therefore + clusters extend to multiple layers of the roll. .. |structured| image:: ../auto_examples/cluster/images/sphx_glr_plot_ward_structured_vs_unstructured_002.png :target: ../auto_examples/cluster/plot_ward_structured_vs_unstructured.html :scale: 49 + :alt: A 3D scatter plot of 1500 data points. Points are located along a swirl + or swiss roll. The six clusters are highlighted in different colours. + The hierarchical clustering is performed with connectivity constraints + that respect the structure of the roll therefore the clusters form a nice + parcellation along the roll. .. centered:: |unstructured| |structured| @@ -798,6 +818,9 @@ by black points below. .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_001.png :target: ../auto_examples/cluster/plot_dbscan.html :scale: 50 + :alt: A bubble chart that maps three clusters, with the x-axis from -2.5 to 2 + and the y-axis from -2 to 2.5. Outliers are indicated separately and are + evenly spaced around the clusters. .. centered:: |dbscan_results| diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index c97676ea62108..23b65b40e427b 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -154,6 +154,9 @@ object to the same sample. :target: ../auto_examples/covariance/plot_covariance_estimation.html :align: center :scale: 65% + :alt: A plot comparing shrinkage coefficients by regularization parameter: + shrinkage coefficient on the x-axis and error: negative log-likelihood on test data + on the y-axis. Bias-variance trade-off when setting the shrinkage: comparing the choices of Ledoit-Wolf and OAS estimators @@ -178,6 +181,12 @@ object to the same sample. :target: ../auto_examples/covariance/plot_lw_vs_oas.html :align: center :scale: 75% + :alt: Two figures. One of them plots the Mean Squared Error difference between a + LedoitWolf and an OAS estimator of the covariance with the y-axis from 0 to 60. + The other one compares the Shrinkage covariance estimation between the LedoitWolf + and OAS, with the y-axis from 0 to 1. In iboth figures the x-axis is the number of + samples from 5 to 30. + .. _sparse_inverse_covariance: @@ -210,6 +219,10 @@ cross-validation to automatically set the ``alpha`` parameter. :target: ../auto_examples/covariance/plot_sparse_cov.html :align: center :scale: 60% + :alt: Various matrix using different estimators: Empircal covariance, Empircal + precision, Ledoit-Wolf covariance, Ledoit-Wolf precision, GraphicaLassoCV + covariance, GraphicaLassoCV precision, True covariance and True Precision. + *A comparison of maximum likelihood, shrinkage and sparse estimates of the covariance and precision matrix in the very small samples @@ -333,10 +346,14 @@ attributes of a :class:`MinCovDet` robust covariance estimator object. .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html :scale: 49% + :alt: Two line charts comparing estimation errors that are made when using various + types of location and covariance estimates on contaminated Gaussian distributed + data sets. .. |mahalanobis| image:: ../auto_examples/covariance/images/sphx_glr_plot_mahalanobis_distances_001.png :target: ../auto_examples/covariance/plot_mahalanobis_distances.html :scale: 49% + :alt: Scatterplot with plotted Mahalanobis distances.