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Hashing feature transformation using Totally Ra...
RandomTreesEmbedding provides a way to map data to a very high-dimensional, sparse representation, which might be beneficial for classification. The mapping is completely unsupervised and very effi...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html -
Comparing random forests and the multi-output m...
An example to compare multi-output regression with random forest and the multioutput.MultiOutputRegressor meta-estimator. This example illustrates the use of the multioutput.MultiOutputRegressor me...scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html -
Lasso model selection: AIC-BIC / cross-validati...
This example focuses on model selection for Lasso models that are linear models with an L1 penalty for regression problems. Indeed, several strategies can be used to select the value of the regular...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html -
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 -
A demo of structured Ward hierarchical clusteri...
Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in one piece. Generate data: Resize it to ...scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.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 -
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...
A comparison for the decision boundaries generated on the iris dataset by Label Spreading, Self-training and SVM. This example demonstrates that Label Spreading and Self-training can learn good bou...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html