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SGD: Maximum margin separating hyperplane ̵...
Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD. Total running time of the script:(0 minutes 0...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
SVM: Separating hyperplane for unbalanced class...
Find the optimal separating hyperplane using an SVC for classes that are unbalanced. We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automa...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html -
1.14. Semi-supervised learning — scikit-l...
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 -
Features in Histogram Gradient Boosting Trees &...
Histogram-Based Gradient Boosting(HGBT) models may be one of the most useful supervised learning models in scikit-learn. They are based on a modern gradient boosting implementation comparable to Li...scikit-learn.org/stable/auto_examples/ensemble/plot_hgbt_regression.html -
2.7. Novelty and Outlier Detection — scik...
Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier), or should be considered as different (it is an ...scikit-learn.org/stable/modules/outlier_detection.html -
Image denoising using kernel PCA — scikit...
This example shows how to use KernelPCA to denoise images. In short, we take advantage of the approximation function learned during fit to reconstruct the original image. We will compare the result...scikit-learn.org/stable/auto_examples/applications/plot_digits_denoising.html -
Outlier detection on a real data set — sc...
This example illustrates the need for robust covariance estimation on a real data set. It is useful both for outlier detection and for a better understanding of the data structure. We selected two ...scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_wine.html -
Poisson regression and non-normal loss — ...
This example illustrates the use of log-linear Poisson regression on the French Motor Third-Party Liability Claims dataset from 1 and compares it with a linear model fitted with the usual least squ...scikit-learn.org/stable/auto_examples/linear_model/plot_poisson_regression_non_normal_loss.html -
Plot Ridge coefficients as a function of the re...
Shows the effect of collinearity in the coefficients of an estimator. Ridge Regression is the estimator used in this example. Each color represents a different feature of the coefficient vector, an...scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html -
Early stopping of Stochastic Gradient Descent &...
Stochastic Gradient Descent is an optimization technique which minimizes a loss function in a stochastic fashion, performing a gradient descent step sample by sample. In particular, it is a very ef...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html