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Elasticsearch 9.3 adds bfloat16 vector support ...
only use 2 bytes. It does this by discarding the lowest 2 bytes...float16 format, which only uses 2 bytes per value. However, this...www.elastic.co/search-labs/blog/bfloat16-vector-support-elasticsearch -
Feature agglomeration vs. univariate selection ...
selection # This example compares 2 dimensionality reduction strategies:.... randn ( n_samples , size ** 2 ) for x in X : # smooth data x...scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.... -
plot_multi_metric_evaluation.ipynb
param_grid={\"min_samples_split\": range(2, 403, 20)},\n scoring=scoring,\n...scoring=scoring,\n refit=\"AUC\",\n n_jobs=2,\n return_train_score=True,\n)\ngs.fit(X,...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.... -
Using KBinsDiscretizer to discretize continuous...
subplots ( ncols = 2 , sharey = True , figsize = (...predict ( line ), linewidth = 2 , color = "green" , label = "linear...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html -
extract_patches_2d — scikit-learn 1.8.0 documen...
( 2 , 2 )) >>> print ( 'Patches shape:...shape )) Patches shape: (272214, 2, 2, 3) >>> # Here are just two...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.image.extract_patches_2d.html -
plot_classifier_comparison.rst.txt
make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1,...rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape)...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt -
HuberRegressor — scikit-learn 1.8.0 documentation
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...penalty is equal to alpha * ||w||^2 . Must be in the range [0, inf)...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html -
Release Highlights for scikit-learn 0.22 — scik...
2. From 1.2, use RocCurveDisplay instead....n_neighbors = 2 ) print ( imputer . fit_transform ( X )) [[1. 2. 4. ]...scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html -
CCA — scikit-learn 1.8.0 documentation
[ 2. , 2. , 2. ], [ 3. , 5. , 4. ]] >>>...y = [[ 0.1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ], [ 11.9 ,...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html -
plot_multi_metric_evaluation.zip
range(2, 403, 20)}, scoring=scoring, refit="AUC", n_jobs=2, re...ax.plot( [ X_axis[best_index], ] * 2, [0, best_score], linestyle="-.",...scikit-learn.org/stable/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip