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Factor Analysis (with rotation) to visualize pa...
vmin =- 1 , vmax = 1 ) ax . set_xticks ([ 0 , 1 , 2 , 3 ])...set_xticks ([ 0 , 1 ]) ax . set_xticklabels ([ "Comp. 1" , "Comp. 2"...scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html -
inplace_swap_row — scikit-learn 1.6.0 documenta...
1 ) >>> csr . todense () matrix([[0,...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.inplace_swap_row.html -
Varying regularization in Multi-layer Perceptro...
logspace ( - 1 , 1 , 5 ) classifiers = [] names..., y_max = X [:, 1 ] . min () - 0.5 , X [:, 1 ] . max () + 0.5...scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html -
HTTP configuration options | APM .NET Agent Ref...
CaptureBody (performance) ( [ 1.0.1 ] Added in 1.0.1. ) For transactions... (performance) ( [ 1.0.1 ] Added in 1.0.1. ) Configures the content...www.elastic.co/guide/en/apm/agent/dotnet/current/config-http.html -
Explicit feature map approximation for RBF kern...
reshape ( - 1 , data . shape [ 1 ]) # title for the plots...y_max]. plt . subplot ( 1 , 3 , i + 1 ) Z = clf . predict ( flat_grid...scikit-learn.org/stable/auto_examples/miscellaneous/plot_kernel_approximation.html -
An example of K-Means++ initialization — scikit...
:: - 1 ] # Calculate seeds from k-means++...side sample data plt . figure ( 1 ) colors = [ "#4EACC5" , "#FF9C34"...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html -
Performance tuning | APM .NET Agent Reference [...
the sample rate is set at 1.0 , meaning all requests are traced....www.elastic.co/guide/en/apm/agent/dotnet/current/performance-tuning.html -
top_k_accuracy_score — scikit-learn 1.6.0 docum...
The best performance is 1 with normalize == True and the...>>> y_true = np . array ([ 0 , 1 , 2 , 2 ]) >>> y_score = np ....scikit-learn.org/stable/modules/generated/sklearn.metrics.top_k_accuracy_score.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 -
Lasso model selection via information criteria ...
lasso_lars_ic [ - 1 ] . criterion_ , n_samples , lasso_lars_ic [ - 1 ] . noise_variance_...lasso_lars_ic [ - 1 ] . alphas_ == lasso_lars_ic [ - 1 ] . alpha_ )[...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars_ic.html