- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 921 - 930 of 5,610 for * (8.01 sec)
-
StackingRegressor — scikit-learn 1.7.2 document...
RidgeCV ()), ... ( 'svr' , LinearSVR ( random_state = 42 )) ... ] >>>...X_test , y_test ) 0.3... fit ( X , y , ** fit_params ) [source] #...scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingRegressor.html -
SelectFromModel — scikit-learn 1.7.2 documentation
- 1.34 , 0.31 ], ... [ - 2.79 , - 0.02 , - 0.85 ], ... [ - 1.34...1.34 , - 0.48 , - 2.55 ], ... [ 1.92 , 1.48 , 0.65 ]] >>> y =...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
Install Elasticsearch with a Debian package | E...
/ ... Examine /etc/apt/sources.list.d/elasticsearch-9.x.list...elasticsearch-9.1.3-amd64.deb.sha512 sudo dpkg -i elasticsearch-9.1.3-amd64.deb...www.elastic.co/docs/deploy-manage/deploy/self-managed/install-elasticsearch-with-debian-package -
Digg - Handmade by human hands using machines
the core. Technology at the edges. We’re building a human-first...invite only. Digg what you love. Discover what's next. Digg is where...www.digg.com/ -
Release Highlights for scikit-learn 1.0 — sciki...
, 0.5 , 0.5 , 0. ], [0. , 0.125, 0.75 , 0.125], [0. , 0. , 0.5...array([[0.5 , 0.5 , 0. , 0. ], [0.125, 0.75 , 0.125, 0. ], [0....scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html -
LinearSVC — scikit-learn 1.7.2 documentation
= 'auto' , tol = 0.0001 , C = 1.0 , multi_class = 'ovr' , fit_intercept...becomes [x_1, ..., x_n, intercept_scaling] , i.e. a “synthetic” feature...scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html -
GammaRegressor — scikit-learn 1.7.2 documentation
float64(0.773) >>> clf . coef_ array([0.073, 0.067]) >>> clf . intercept_...intercept_ np.float64(2.896) >>> clf . predict ([[ 1 , 0 ], [ 2 , 8 ]])...scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html -
HuberRegressor — scikit-learn 1.7.2 documentation
HuberRegressor () . fit ( X , y ) >>> huber . score ( X , y ) -7.284 >>>...20 , ( 4 , 2 )) >>> y [: 4 ] = rng . uniform ( 10 , 20 , 4 ) >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html -
PolynomialFeatures — scikit-learn 1.7.2 documen...
) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5.,...1., 2., 3., 4., 6., 9.], [ 1., 4., 5., 16., 20., 25.]]) >>> poly...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
median_absolute_error — scikit-learn 1.7.2 docu...
= [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>..., 1 ], [ 7 , - 6 ]] >>> y_pred = [[ 0 , 2 ], [ - 1 , 2 ], [ 8...scikit-learn.org/stable/modules/generated/sklearn.metrics.median_absolute_error.html