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Comparison of LDA and PCA 2D projection of Iris...
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA)...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
Post pruning decision trees with cost complexit...
The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfitting. Cost complexity pruning provides another option to control the size of a tr...scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html -
t-SNE: The effect of various perplexity values ...
An illustration of t-SNE on the two concentric circles and the S-curve datasets for different perplexity values. We observe a tendency towards clearer shapes as the perplexity value increases. The ...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
Illustration of Gaussian process classification...
This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains consi...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html -
3.3. Tuning the decision threshold for class pr...
Classification is best divided into two parts: the statistical problem of learning a model to predict, ideally, class probabilities;, the decision problem to take concrete action based on those pro...scikit-learn.org/stable/modules/classification_threshold.html -
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 -
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 -
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 -
3.1. Cross-validation: evaluating estimator per...
Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the samples that it has just seen would ha...scikit-learn.org/stable/modules/cross_validation.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