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1.7. Gaussian Processes — scikit-learn 1.8.0 do...
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction i...scikit-learn.org/stable/modules/gaussian_process.html -
7.6. Random Projection — scikit-learn 1.8.0 doc...
The sklearn.random_projection module implements a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional varianc...scikit-learn.org/stable/modules/random_projection.html -
Shrinkage covariance estimation: LedoitWolf vs ...
When working with covariance estimation, the usual approach is to use a maximum likelihood estimator, such as the EmpiricalCovariance. It is unbiased, i.e. it converges to the true (population) cov...scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html -
Concentration Prior Type Analysis of Variation ...
This example plots the ellipsoids obtained from a toy dataset (mixture of three Gaussians) fitted by the BayesianGaussianMixture class models with a Dirichlet distribution prior ( weight_concentrat...scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html -
Imputing missing values with variants of Iterat...
The IterativeImputer class is very flexible - it can be used with a variety of estimators to do round-robin regression, treating every variable as an output in turn. In this example we compare some...scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html -
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: 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... -
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 -
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