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neighbors.rst.txt
[-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> nbrs...np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> kdt...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
trustworthiness — scikit-learn 1.8.0 docu...
defined as \[T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}...Should be fewer than n_samples / 2 to ensure the trustworthiness...scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html -
Plot the decision surfaces of ensembles of tree...
2 , w_pad = 0.2 , pad = 2.5 ) plt . show ()...pair in ([ 0 , 1 ], [ 0 , 2 ], [ 2 , 3 ]): for model in models...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html -
ParameterSampler — scikit-learn 1.8.0 doc...
'a' : 2 }, ... { 'b' : 1.038159 , 'a' : 2 }] True...param_grid = { 'a' :[ 1 , 2 ], 'b' : expon ()} >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterSampler.html -
hamming_loss — scikit-learn 1.8.0 documen...
2 , 3 , 4 ] >>> y_true = [ 2 , 2 , 3 , 4 ]...[ 1 , 1 ]]), np . zeros (( 2 , 2 ))) 0.75 Gallery examples #...scikit-learn.org/stable/modules/generated/sklearn.metrics.hamming_loss.html -
johnson_lindenstrauss_min_dim — scikit-le...
v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2 Where...>= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3) Note that the number...scikit-learn.org/stable/modules/generated/sklearn.random_projection.johnson_lindenstrauss_min_dim... -
Install IBM Storage Scale Container Native 6.0....
worker - 1 2 / 2 Running 0 6 m39s worker - 2 2 / 2 Running 0 6...llector- 1 2 / 2 Running 0 4 m6s worker - 0 2 / 2 Running 0 6...developer.ibm.com/tutorials/install-spectrum-scale-cnsa-5121-on-ocp-48-on-powervs/ -
HuberRegressor vs Ridge on dataset with strong ...
2.0 , size = 4 ) X_outliers [: 2 , :] += X . max...y_outliers [: 2 ] += y . min () - y . mean () / 4.0 y_outliers [ 2 :] +=...scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html -
Concentration Prior Type Analysis of Variation ...
normalization eig_vals = 2 * np . sqrt ( 2 ) * np . sqrt ( eig_vals...= 0.8 ) ax1 . set_xlim ( - 2.0 , 2.0 ) ax1 . set_ylim ( - 3.0...scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html -
lasso_path — scikit-learn 1.8.0 documenta...
it is: ( 1 / ( 2 * n_samples )) * || Y - XW ||^ 2 _Fro + alpha...X = np . array ([[ 1 , 2 , 3.1 ], [ 2.3 , 5.4 , 4.3 ]]) . T >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lasso_path.html