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Multi-dimensional scaling — scikit-learn ...
An illustration of the metric and non-metric MDS on generated noisy data. Dataset preparation: We start by uniformly generating 20 points in a 2D space. Now we compute pairwise distances between al...scikit-learn.org/stable/auto_examples/manifold/plot_mds.html -
SVM with custom kernel — scikit-learn 1.8...
Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface and the support vectors. Total running time of the script:(0 minutes 0.077 seconds) Launch binder Lau...scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html -
Support Vector Machines — scikit-learn 1....
Examples concerning the sklearn.svm module. One-class SVM with non-linear kernel (RBF) Plot classification boundaries with different SVM Kernels Plot different SVM classifiers in the iris dataset P...scikit-learn.org/stable/auto_examples/svm/index.html -
Ability of Gaussian process regression (GPR) to...
This example shows the ability of the WhiteKernel to estimate the noise level in the data. Moreover, we show the importance of kernel hyperparameters initialization. Data generation: We will work i...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html -
7.7. Kernel Approximation — scikit-learn ...
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 -
1.8. Cross decomposition — scikit-learn 1...
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 -
1.15. Isotonic regression — scikit-learn ...
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 -
OOB Errors for Random Forests — scikit-le...
The RandomForestClassifier is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations z_i = (x_i, y_i). The out-of-bag(OOB) error is the...scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html -
An example of K-Means++ initialization — ...
An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating initial seeds for clustering. K-Means++ is used as the default initialization for K-means. Total running...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html -
Time-related feature engineering — scikit...
This notebook introduces different strategies to leverage time-related features for a bike sharing demand regression task that is highly dependent on business cycles (days, weeks, months) and yearl...scikit-learn.org/stable/auto_examples/applications/plot_cyclical_feature_engineering.html