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Comparing randomized search and grid search for...
Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with SGD training. All parameters that influence the learning are searched simultaneously (except for the nu...scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html -
Shrinkage covariance estimation: LedoitWolf vs ...
When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the EmpiricalCovariance. It is unbiased, i.e. it converges to the true (population) cov...scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html -
Concentration Prior Type Analysis of Variation ...
This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the BayesianGaussianMixture class models with a Dirichlet distribution prior ( weight_concentrat...scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html -
3.5. Validation curves: plotting scores to eval...
Every estimator has its advantages and drawbacks. Its generalization error can be decomposed in terms of bias, variance and noise. The bias of an estimator is its average error for different traini...scikit-learn.org/stable/modules/learning_curve.html -
Decision Boundaries of Multinomial and One-vs-R...
This example compares decision boundaries of multinomial and one-vs-rest logistic regression on a 2D dataset with three classes. We make a comparison of the decision boundaries of both methods that...scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html -
Comparing anomaly detection algorithms for outl...
This example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to c...scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html -
Decision boundary of semi-supervised classifier...
This example compares decision boundaries learned by two semi-supervised methods, namely LabelSpreading and SelfTrainingClassifier, while varying the proportion of labeled training data from small ...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
1.11. Ensembles: Gradient boosting, random fore...
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. Two very famous ...scikit-learn.org/stable/modules/ensemble.html -
One-Class SVM versus One-Class SVM using Stocha...
This example shows how to approximate the solution of sklearn.svm.OneClassSVM in the case of an RBF kernel with sklearn.linear_model.SGDOneClassSVM, a Stochastic Gradient Descent (SGD) version of t...scikit-learn.org/stable/auto_examples/linear_model/plot_sgdocsvm_vs_ocsvm.html -
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