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Overview of multiclass training meta-estimators...
In this example, we discuss the problem of classification when the target variable is composed of more than two classes. This is called multiclass classification. In scikit-learn, all estimators su...scikit-learn.org/stable/auto_examples/multiclass/plot_multiclass_overview.html -
Label Propagation digits: Demonstrating perform...
This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels. The handwritten digit dataset has 179...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.html -
Label Propagation digits: Active learning — sci...
Demonstrates an active learning technique to learn handwritten digits using label propagation. We start by training a label propagation model with only 10 labeled points, then we select the top fiv...scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learni... -
SVM: Maximum margin separating hyperplane — sci...
Plot the maximum margin separating hyperplane within a two-class separable dataset using a Support Vector Machine classifier with linear kernel. Total running time of the script:(0 minutes 0.053 se...scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html -
7.4. Imputation of missing values — scikit-lear...
For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders. Such datasets however are incompatible with scikit-learn estimators which ...scikit-learn.org/stable/modules/impute.html -
1.3. Kernel ridge regression — scikit-learn 1.8...
Kernel ridge regression (KRR)[M2012] combines Ridge regression and classification(linear least squares with L_2-norm regularization) with the kernel trick. It thus learns a linear function in the s...scikit-learn.org/stable/modules/kernel_ridge.html -
Model selection with Probabilistic PCA and Fact...
Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the likelihood of new data can be used for model selection and covariance estimation. Here we compare PCA and...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html -
Comparing Random Forests and Histogram Gradient...
In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boosting (HGBT) models in terms of score and computation time for a regression dataset, though all the concep...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.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 -
Plot the decision surfaces of ensembles of tree...
Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset. This plot compares the decision surfaces learned by a decision tree classifier (first col...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html