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Imputing missing values before building an esti...
Missing values can be replaced by the mean, the median or the most frequent value using the basic SimpleImputer. In this example we will investigate different imputation techniques: imputation by t...scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html -
Plot different SVM classifiers in the iris data...
Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset: Sepal length, Sepal width. This example shows how to pl...scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html -
9.2. Computational Performance — scikit-l...
Scipy’s sparse matrix formats documentation for more information on...beyond the scope of this documentation though. 9.2.3. Tips and...scikit-learn.org/stable/computing/computational_performance.html -
Single estimator versus bagging: bias-variance ...
This example illustrates and compares the bias-variance decomposition of the expected mean squared error of a single estimator against a bagging ensemble. In regression, the expected mean squared e...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html -
Illustration of prior and posterior Gaussian pr...
This example illustrates the prior and posterior of a GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions. ...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html -
Compressive sensing: tomography reconstruction ...
This example shows the reconstruction of an image from a set of parallel projections, acquired along different angles. Such a dataset is acquired in computed tomography(CT). Without any prior infor...scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html -
1.3. Kernel ridge regression — scikit-lea...
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 — sciki...
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 -
Prediction Intervals for Gradient Boosting Regr...
This example shows how quantile regression can be used to create prediction intervals. See Features in Histogram Gradient Boosting Trees for an example showcasing some other features of HistGradien...scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html -
Multi-class AdaBoosted Decision Trees — s...
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