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sklearn.kernel_approximation — scikit-learn 1.7...
Approximate kernel feature maps based on Fourier transforms and count sketches. User guide. See the Kernel Approximation section for further details.scikit-learn.org/stable/api/sklearn.kernel_approximation.html -
fetch_species_distributions — scikit-learn 1.7....
Gallery examples: Species distribution modeling Kernel Density Estimate of Species Distributionsscikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_species_distributions.html -
Ledoit-Wolf vs OAS estimation — scikit-learn 1....
The usual covariance maximum likelihood estimate can be regularized using shrinkage. Ledoit and Wolf proposed a close formula to compute the asymptotically optimal shrinkage parameter (minimizing a...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html -
Effect of varying threshold for self-training —...
This example illustrates the effect of a varying threshold on self-training. The breast_cancer dataset is loaded, and labels are deleted such that only 50 out of 569 samples have labels. A SelfTrai...scikit-learn.org/stable/auto_examples/semi_supervised/plot_self_training_varying_threshold.html -
A demo of the mean-shift clustering algorithm —...
Reference: Dorin Comaniciu and Peter Meer, “Mean Shift: A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. pp. 603-619. Generate...scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html -
Model-based and sequential feature selection — ...
This example illustrates and compares two approaches for feature selection: SelectFromModel which is based on feature importance, and SequentialFeatureSelector which relies on a greedy approach. We...scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_diabetes.html -
Imputing missing values before building an esti...
Missing values can be replaced by the mean, the median or the most frequent value using the basic SimpleImputer. In this example we will investigate different imputation techniques: imputation by t...scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html -
7.3. Preprocessing data — scikit-learn 1.7.1 do...
The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream esti...scikit-learn.org/stable/modules/preprocessing.html -
1.13. Feature selection — scikit-learn 1.7.1 do...
The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their perfor...scikit-learn.org/stable/modules/feature_selection.html -
Plot different SVM classifiers in the iris data...
Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset: Sepal length, Sepal width. This example shows how to pl...scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html