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sklearn.calibration — scikit-learn 1.7.2 docume...
Methods for calibrating predicted probabilities. User guide. See the Probability calibration section for further details. Visualization:scikit-learn.org/stable/api/sklearn.calibration.html -
SGD: Maximum margin separating hyperplane — sci...
linspace ( - 1 , 5 , 10 ) yy = np . linspace ( - 1 , 5 , 10 ) X1...= p [ 0 ] levels = [ - 1.0 , 0.0 , 1.0 ] linestyles = [ "dashed"...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html -
移行 0.9.5 to 0.9.5.1 | DBFlute
1 お約束の注意点 環境上の注意点 実装上の注意点 お約束の注意点...dbflute.seasar.org/ja/environment/upgrade/migration/migrate0950to0951.html -
calinski_harabasz_score — scikit-learn 1.7.2 do...
References [ 1 ] T. Calinski and J. Harabasz,...scikit-learn.org/stable/modules/generated/sklearn.metrics.calinski_harabasz_score.html -
ledoit_wolf_shrinkage — scikit-learn 1.7.2 docu...
regularized (shrunk) covariance is: (1 - shrinkage) * cov + shrinkage...scikit-learn.org/stable/modules/generated/sklearn.covariance.ledoit_wolf_shrinkage.html -
Lasso, Lasso-LARS, and Elastic Net paths — scik...
legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso" , "LARS" ),...plt . legend (( l1 [ - 1 ], l2 [ - 1 ]), ( "Lasso" , "Elastic-Net"...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lasso_lars_elasticnet_path.html -
SVM-Anova: SVM with univariate feature selectio...
score_stds = list () percentiles = ( 1 , 3 , 6 , 10 , 15 , 20 , 30 ,...scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html -
inplace_csr_row_normalize_l2 — scikit-learn 1.7...
1 , 2 , 3 ]) >>> data = np . array ([ 1.0 , 2.0 , 3.0...0. ], [0. , 0. , 1. , 0. ], [0. , 0. , 0. , 1. ]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
Probability Calibration for 3-class classificat...
"($\frac {1}{3} $, $\frac {1}{3} $, $\frac {1}{3} $)" , xy = ( 1.0 /...r "($1$, $0$, $0$)" , xy = ( 1 , 0 ), xytext = ( 1 , 0.1 ), xycoords...scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html -
RadiusNeighborsClassifier — scikit-learn 1.7.2 ...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...[[ 0 ], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>> from...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html