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t-SNE: The effect of various perplexity values ...
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 perplexity value increases. The ...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
Compressive sensing: tomography reconstruction ...
This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography(CT). Without any prior infor...scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html -
7.9. Transforming the prediction target (y) — s...
Transforming the prediction target ( y): These are transformers that are not intended to be used on features, only on supervised learning targets. See also Transforming target in regression if you ...scikit-learn.org/stable/modules/preprocessing_targets.html -
1.2. Linear and Quadratic Discriminant Analysis...
Linear Discriminant Analysis ( LinearDiscriminantAnalysis) and Quadratic Discriminant Analysis ( QuadraticDiscriminantAnalysis) are two classic classifiers, with, as their names suggest, a linear a...scikit-learn.org/stable/modules/lda_qda.html -
1.14. Semi-supervised learning — scikit-learn 1...
Semi-supervised learning is a situation in which in your training data some of the samples are not labeled. The semi-supervised estimators in sklearn.semi_supervised are able to make use of this ad...scikit-learn.org/stable/modules/semi_supervised.html -
Gaussian process classification (GPC) on iris d...
This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly ...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html -
Comparing anomaly detection algorithms for outl...
This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to c...scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html -
Novelty detection with Local Outlier Factor (LO...
The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as ...scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html -
Visualizing cross-validation behavior in scikit...
Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in order to avoid model overfitting, to stand...scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html -
Comparing random forests and the multi-output m...
An example to compare multi-output regression with random forest and the multioutput.MultiOutputRegressor meta-estimator. This example illustrates the use of the multioutput.MultiOutputRegressor me...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html