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Approximate nearest neighbors in TSNE — scikit-...
This example presents how to chain KNeighborsTransformer and TSNE in a pipeline. It also shows how to wrap the packages nmslib and pynndescent to replace KNeighborsTransformer and perform approxima...scikit-learn.org/stable/auto_examples/neighbors/approximate_nearest_neighbors.html -
7.7. Kernel Approximation — scikit-learn 1.8.0 ...
This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see Support Vector Machines). Th...scikit-learn.org/stable/modules/kernel_approximation.html -
Plot the support vectors in LinearSVC — scikit-...
Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vectors. This example demonstrates how to obtain the support vectors in LinearSVC. Total running time of th...scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html -
1.15. Isotonic regression — scikit-learn 1.8.0 ...
The class IsotonicRegression fits a non-decreasing real function to 1-dimensional data. It solves the following problem:\min \sum_i w_i (y_i - \hat{y}_i)^2 subject to\hat{y}_i \le \hat{y}_j wheneve...scikit-learn.org/stable/modules/isotonic.html -
1.8. Cross decomposition — scikit-learn 1.8.0 d...
The cross decomposition module contains supervised estimators for dimensionality reduction and regression, belonging to the “Partial Least Squares” family. Cross decomposition algorithms find the f...scikit-learn.org/stable/modules/cross_decomposition.html -
Gradient Boosting regression — scikit-learn 1.8...
This example demonstrates Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here,...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html -
Generalized Linear Models — scikit-learn 1.8.0 ...
Examples concerning the sklearn.linear_model module. Comparing Linear Bayesian Regressors Curve Fitting with Bayesian Ridge Regression Decision Boundaries of Multinomial and One-vs-Rest Logistic Re...scikit-learn.org/stable/auto_examples/linear_model/index.html -
__sklearn_is_fitted__ as Developer API — scikit...
The__sklearn_is_fitted__ method is a convention used in scikit-learn for checking whether an estimator object has been fitted or not. This method is typically implemented in custom estimator classe...scikit-learn.org/stable/auto_examples/developing_estimators/sklearn_is_fitted.html -
Polynomial and Spline interpolation — scikit-le...
This example demonstrates how to approximate a function with polynomials up to degree degree by using ridge regression. We show two different ways given n_samples of 1d points x_i: PolynomialFeatur...scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html -
Recursive feature elimination — scikit-learn 1....
This example demonstrates how Recursive Feature Elimination ( RFE) can be used to determine the importance of individual pixels for classifying handwritten digits. RFE recursively removes the least...scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html