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Multi-class AdaBoosted Decision Trees — scikit-...
This example shows how boosting can improve the prediction accuracy on a multi-label classification problem. It reproduces a similar experiment as depicted by Figure 1 in Zhu et al 1. The core prin...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html -
Robust covariance estimation and Mahalanobis di...
This example shows covariance estimation with Mahalanobis distances on Gaussian distributed data. For Gaussian distributed data, the distance of an observation x_i to the mode of the distribution c...scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html -
Comparison between grid search and successive h...
This example compares the parameter search performed by HalvingGridSearchCV and GridSearchCV. We first define the parameter space for an SVC estimator, and compute the time required to train a Halv...scikit-learn.org/stable/auto_examples/model_selection/plot_successive_halving_heatmap.html -
Support Vector Regression (SVR) using linear an...
Toy example of 1D regression using linear, polynomial and RBF kernels. Generate sample data: Fit regression model: Look at the results: Total running time of the script:(0 minutes 5.541 seconds) La...scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html -
MNIST classification using multinomial logistic...
Here we fit a multinomial logistic regression with L1 penalty on a subset of the MNIST digits classification task. We use the SAGA algorithm for this purpose: this a solver that is fast when the nu...scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html -
1.3. Kernel ridge regression — scikit-learn 1.7...
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 -
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 -
adjusted_mutual_info_score — scikit-learn 1.7.2...
Gallery examples: Adjustment for chance in clustering performance evaluation Demo of affinity propagation clustering algorithm Demo of DBSCAN clustering algorithm A demo of K-Means clustering on th...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html -
Gaussian Processes regression: basic introducto...
A simple one-dimensional regression example computed in two different ways: A noise-free case, A noisy case with known noise-level per datapoint. In both cases, the kernel’s parameters are estimate...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html -
Hierarchical clustering: structured vs unstruct...
Example builds a swiss roll dataset and runs hierarchical clustering on their position. For more information, see Hierarchical clustering. In a first step, the hierarchical clustering is performed ...scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html