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Kernel PCA — scikit-learn 1.8.0 documenta...
This example shows the difference between the Principal Components Analysis ( PCA) and its kernelized version ( KernelPCA). On the one hand, we show that KernelPCA is able to find a projection of t...scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html -
sklearn.cluster — scikit-learn 1.8.0 docu...
Popular unsupervised clustering algorithms. User guide. See the Clustering and Biclustering sections for further details.scikit-learn.org/stable/api/sklearn.cluster.html -
sklearn.decomposition — scikit-learn 1.8....
Matrix decomposition algorithms. These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be regarded as dimensionality reduction techniques. User guide. See the Decomposing...scikit-learn.org/stable/api/sklearn.decomposition.html -
sklearn.calibration — scikit-learn 1.8.0 ...
Methods for calibrating predicted probabilities. User guide. See the Probability calibration section for further details. Visualization:scikit-learn.org/stable/api/sklearn.calibration.html -
9.1. Strategies to scale computationally: bigge...
beyond the scope of this documentation. 9.1.1.2. Extracting features...shingVectorizer for text documents. 9.1.1.3. Incremental learning...scikit-learn.org/stable/computing/scaling_strategies.html -
5. Inspection — scikit-learn 1.8.0 docume...
Predictive performance is often the main goal of developing machine learning models. Yet summarizing performance with an evaluation metric is often insufficient: it assumes that the evaluation metr...scikit-learn.org/stable/inspection.html -
12. Dispatching — scikit-learn 1.8.0 docu...
Array API support (experimental)- Enabling array API support, Example usage, Support for Array API-compatible inputs, Input and output array type handling, Common estimator checks..scikit-learn.org/stable/dispatching.html -
Release Highlights — scikit-learn 1.8.0 d...
These examples illustrate the main features of the releases of scikit-learn. Release Highlights for scikit-learn 1.8 Release Highlights for scikit-learn 1.7 Release Highlights for scikit-learn 1.6 ...scikit-learn.org/stable/auto_examples/release_highlights/index.html -
Cross decomposition — scikit-learn 1.8.0 ...
Examples concerning the sklearn.cross_decomposition module. Compare cross decomposition methods Principal Component Regression vs Partial Least Squares Regressionscikit-learn.org/stable/auto_examples/cross_decomposition/index.html -
Inductive Clustering — scikit-learn 1.8.0...
Clustering can be expensive, especially when our dataset contains millions of datapoints. Many clustering algorithms are not inductive and so cannot be directly applied to new data samples without ...scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html