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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 -
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 -
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 -
shuffle — scikit-learn 1.7.2 documentation
Gallery examples: Prediction Latency Empirical evaluation of the impact of k-means initialization Combine predictors using stacking Early stopping of Stochastic Gradient Descent Approximate nearest...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
Tags — scikit-learn 1.7.2 documentation
Gallery examples: Release Highlights for scikit-learn 1.6scikit-learn.org/stable/modules/generated/sklearn.utils.Tags.html -
TargetTags — scikit-learn 1.7.2 documentation
Skip to main content Back to top Ctrl + K GitHub Choose version TargetTags # class sklearn.utils. TargetTags ( requir...scikit-learn.org/stable/modules/generated/sklearn.utils.TargetTags.html -
Preprocessing — scikit-learn 1.7.2 documentation
Examples concerning the sklearn.preprocessing module. Compare the effect of different scalers on data with outliers Comparing Target Encoder with Other Encoders Demonstrating the different strategi...scikit-learn.org/stable/auto_examples/preprocessing/index.html -
Clustering — scikit-learn 1.7.2 documentation
Examples concerning the sklearn.cluster module. A demo of K-Means clustering on the handwritten digits data A demo of structured Ward hierarchical clustering on an image of coins A demo of the mean...scikit-learn.org/stable/auto_examples/cluster/index.html -
Decomposition — scikit-learn 1.7.2 documentation
Examples concerning the sklearn.decomposition module. Blind source separation using FastICA Comparison of LDA and PCA 2D projection of Iris dataset Faces dataset decompositions Factor Analysis (wit...scikit-learn.org/stable/auto_examples/decomposition/index.html -
LocallyLinearEmbedding — scikit-learn 1.7.2 doc...
Gallery examples: Visualizing the stock market structure Comparison of Manifold Learning methods Manifold learning on handwritten digits: Locally Linear Embedding, Isomap… Manifold Learning methods...scikit-learn.org/stable/modules/generated/sklearn.manifold.LocallyLinearEmbedding.html