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Tweedie regression on insurance claims — scikit...
tweedie_powers = [ 1.5 , 1.7 , 1.8 , 1.9 , 1.99 , 1.999 , 1.9999 ] scores_product_model...dev p=1.9990 1.914574e+03 1.914370e+03 1.914537e+03 1.914388e+03...scikit-learn.org/stable/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html -
TestExecutionListeners.MergeMode (Spring Framew...
1 Author: Sam Brannen Nested Class...docs.spring.io/spring-framework/docs/current/javadoc-api/org/springframework/test/context/TestExe... -
Comparison of LDA and PCA 2D projection of Iris...
1 , 2 ], target_names ): plt . scatter...y == i , 0 ], X_r [ y == i , 1 ], color = color , alpha = 0.8...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
Version 0.18 — scikit-learn 1.8.0 documentation
for compatibility with NumPy 1.13.0: #7946 #8355 by Loic Esteve...Guerin, Sergei Lebedev Version 0.18.1 # November 11, 2016 Changelog...scikit-learn.org/stable/whats_new/v0.18.html -
Illustration of Gaussian process classification...
kernels = [ 1.0 * RBF ( length_scale = 1.15 ), 1.0 * DotProduct...)[:, 1 ] Z = Z . reshape ( xx . shape ) plt . subplot ( 1 , 2...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html -
Displaying estimators and complex pipelines — s...
l1_ratio = 1 ) print ( lr ) LogisticRegression(l1_ratio=1) Rich HTML..... versionadded:: 1.0 .. versionchanged:: 1.6 `verbose_feature_names_out`...scikit-learn.org/stable/auto_examples/miscellaneous/plot_estimator_representation.html -
Robust vs Empirical covariance estimate — sciki...
5 )[ 1 : - 1 ], ) ) . astype ( int ) # definition...= 11 ) plt . subplot ( 2 , 1 , 1 ) lw = 2 plt . errorbar ( range_n_outliers...scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html -
Swiss Roll And Swiss-Hole Reduction — scikit-le...
) axs [ 1 ] . scatter ( sr_tsne [:, 0 ], sr_tsne [:, 1 ], c =...) axs [ 1 ] . scatter ( sh_tsne [:, 0 ], sh_tsne [:, 1 ], c =...scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html -
3.4. Metrics and scoring: quantifying the quali...
1 , 1 , 1 , 1 , 1 ] >>> y_pred = [ 0 , 1 , 0 , 1 , 0 ,..., 0 , 1 , 1 , 1 , 1 , 1 ] >>> y_pred = [ 0 , 1 , 0 , 1 , 0 ,...scikit-learn.org/stable/modules/model_evaluation.html -
Illustration of prior and posterior Gaussian pr...
"Observations" ) axs [ 1 ] . legend ( bbox_to_anchor = ( 1.05 , 1.5 ), loc..."Observations" ) axs [ 1 ] . legend ( bbox_to_anchor = ( 1.05 , 1.5 ), loc...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html