- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 1001 - 1010 of 2,960 for 1 (0.46 sec)
-
sklearn — scikit-learn 1.6.1 documentation
Configure global settings and get information about the working environment.scikit-learn.org/stable/api/sklearn.html -
Lasso on dense and sparse data — scikit-learn 1...
coo_matrix ( X ) alpha = 1 sparse_lasso = Lasso ( alpha =...Distance between coefficients : 1.01e-13 Comparing the two Lasso...scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html -
Visualization of MLP weights on MNIST — scikit-...
Iteration 1, loss = 0.44139186 Iteration 2,...fetch_openml ( "mnist_784" , version = 1 , return_X_y = True , as_frame...scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html -
Density Estimation for a Gaussian mixture — sci...
norm = LogNorm ( vmin = 1.0 , vmax = 1000.0 ), levels =...X_train [:, 0 ], X_train [:, 1 ], 0.8 ) plt . title ( "Negative...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html -
Map data to a normal distribution — scikit-lear...
1 ) # lognormal distribution X_lognormal.... uniform ( low = 0 , high = 1 , size = size ) # bimodal distribution...scikit-learn.org/stable/auto_examples/preprocessing/plot_map_data_to_normal.html -
Concatenating multiple feature extraction metho...
features__univ_select__k=1, svm__C=0.1 [CV 1/5; 1/18] END features_...nts=1, features__univ_select__k=1, svm__C=0.1;, score=1.000 total...scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html -
Feature importances with a forest of trees — sc...
shape [ 1 ])] forest = RandomForestClassifi...time of the script: (0 minutes 1.099 seconds) Download Jupyter...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html -
Plot class probabilities calculated by the Voti...
[ 1.1 , 1.2 ]]) y = np . array ([ 1 , 1 , 2 , 2 ]) eclf...np . array ([[ - 1.0 , - 1.0 ], [ - 1.2 , - 1.4 ], [ - 3.4 , -...scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html -
Demonstration of k-means assumptions — scikit-l...
axs [ 1 , 0 ] . set_title ( "Unequal Variance" ) axs [ 1 , 1 ] ....X_filtered [:, 1 ], c = y_filtered ) axs [ 1 , 1 ] . set_title...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html -
add_dummy_feature — scikit-learn 1.6.1 document...
1 ], [ 1 , 0 ]]) array([[1., 0., 1.], [1., 1., 0.]])...add_dummy_feature ( X , value = 1.0 ) [source] # Augment dataset...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.add_dummy_feature.html