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Theil-Sen Regression — scikit-learn 1.5.2 docum...
Linear model y = 3*x + N(2, 0.1**2) x = np . random . randn (...Linear model y = 3*x + N(2, 0.1**2) x = np . random . randn (...scikit-learn.org/stable/auto_examples/linear_model/plot_theilsen.html -
Linear and Quadratic Discriminant Analysis with...
[ 2.5 , 0.7 ]]) * 2.0 cov_class_2 = cov_class_1 ....n_features = 2 , cov_class_1 = covariance , cov_class_2 = covariance...scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html -
Tweedie regression on insurance claims — scikit...
9900 2.015716e+02 2.015414e+02 2.015347e+02 2.015587e+02...abs. error 2.730119e+02 2.722128e+02 2.739865e+02 2.731249e+02...scikit-learn.org/stable/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html -
Recently Deprecated — scikit-learn 1.5.2 docume...
To be removed in 1.7scikit-learn.org/stable/api/deprecated.html -
Model Selection — scikit-learn 1.5.2 documentation
Examples related to the sklearn.model_selection module. Balance model complexity and cross-validated score Class Likelihood Ratios to measure classification performance Comparing randomized search ...scikit-learn.org/stable/auto_examples/model_selection/index.html -
Tutorial exercises — scikit-learn 1.5.2 documen...
Exercises for the tutorials Cross-validation on diabetes Dataset Exercise Digits Classification Exercise SVM Exercisescikit-learn.org/stable/auto_examples/exercises/index.html -
8.1. Strategies to scale computationally: bigge...
2. Extracting features # 2. could be any relevant...scikit-learn.org/stable/computing/scaling_strategies.html -
Robust covariance estimation and Mahalanobis di...
standard deviation equal to 2 and feature 2 has a standard deviation...n_features = 2 # generate Gaussian data of shape (125, 2) gen_cov...scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html -
Plot classification boundaries with different S...
2 , 0.5 ], [ 0.2 , - 2.0 ], [ 0.5 , - 2.4 ], [ 0.2 , - 2.3...[ - 1.3 , - 1.2 ], [ - 1.1 , - 0.2 ], [ - 1.2 , - 0.4 ], [ -...scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html -
Single estimator versus bagging: bias-variance ...
- ( x ** 2 )) + 1.5 * np . exp ( - (( x - 2 ) ** 2 )) def generate...{0} : {1:.4f} (error) = {2:.4f} (bias^2) " " + {3:.4f} (var) +...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html