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7.3. Preprocessing data — scikit-learn 1....
2. , 1. , 0.1], [4.4, 2.2, 1.1, 0.1], [4.4, 2.2, 1.2, 0.1],...4.1, 6.7, 2.5], [7.7, 4.2, 6.7, 2.5], [7.9, 4.4, 6.9, 2.5]]) Thus...scikit-learn.org/stable/modules/preprocessing.html -
LassoLarsIC — scikit-learn 1.8.0 document...
[[ - 2 , 2 ], [ - 1 , 1 ], [ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]] >>>...>>> y = [ - 2.2222 , - 1.1111 , 0 , - 1.1111 , - 2.2222 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoLarsIC.html -
Linear and Quadratic Discriminant Analysis with...
[ 2.5 , 0.7 ]]) * 2.0 cov_class_2 = cov_class_1.... Ellipse ( mean , 2 * v [ 0 ] ** 0.5 , 2 * v [ 1 ] ** 0.5 ,...scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html -
Ledoit-Wolf vs OAS estimation — scikit-le...
subplot ( 2 , 1 , 2 ) plt . errorbar ( n_samples_range...shrinkage_ # plot MSE plt . subplot ( 2 , 1 , 1 ) plt . errorbar ( n_samples_range...scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html -
Theil-Sen Regression — scikit-learn 1.8.0...
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 -
Overview of multiclass training meta-estimators...
code_size = 2 ) cv_results_tree = cross_validate..., X , y , cv = cv , n_jobs = 2 ) cv_results_ovo = cross_validate...scikit-learn.org/stable/auto_examples/multiclass/plot_multiclass_overview.html -
Spectral clustering for image segmentation R...
]) ** 2 + ( y - center1 [ 1 ]) ** 2 < radius1 ** 2 circle2...]) ** 2 + ( y - center2 [ 1 ]) ** 2 < radius2 ** 2 circle3...scikit-learn.org/stable/auto_examples/cluster/plot_segmentation_toy.html -
top_k_accuracy_score — scikit-learn 1.8.0...
2 , 0.2 ], # 0 is in top 2 ... [ 0.3 , 0.4 , 0.2 ], # 1...is in top 2 ... [ 0.2 , 0.4 , 0.3 ], # 2 is in top 2 ... [ 0.7...scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.html -
DistanceMetric — scikit-learn 1.8.0 docum...
HaversineDistance 2 arcsin(sqrt(sin^2(0.5*dx) + cos(x1)cos(x2)sin^2(0.5*dy)))...'euclidean' ) >>> X = [[ 1 , 2 ], [ 3 , 4 ], [ 5 , 6 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html -
7.4. Imputation of missing values — sciki...
2. Univariate feature imputation...>>> imp . fit ([[ 1 , 2 ], [ np . nan , 3 ], [ 7 , 6 ]])...scikit-learn.org/stable/modules/impute.html