- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 41 - 50 of 1,427 for = (0.05 sec)
-
ElasticNetCV — scikit-learn 1.5.0 documentation
l1_ratio = 0.5 , eps = 0.001 , n_alphas = 100 , alphas = None ,...fit_intercept = True , precompute = 'auto' , max_iter = 1000 , tol = 0.0001...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html -
Target Encoder’s Internal Cross fitting — sciki...
transform_output = "pandas" ) ridge = Ridge ( alpha = 1e-6 , solver = "lsqr"...n_samples = 50_000 rng = np . random . RandomState ( 42 ) y = rng ....scikit-learn.org/stable/auto_examples/preprocessing/plot_target_encoder_cross_val.html -
Scalable learning with polynomial kernel approx...
y = fetch_covtype ( return_X_y = True ) y [ y != 2 ] = 0 y...SVC ksvm = SVC ( C = 500.0 , kernel = "poly" , degree = 4 , coef0...scikit-learn.org/stable/auto_examples/kernel_approximation/plot_scalable_poly_kernels.html -
Restricted Boltzmann Machine features for digit...
mode = "constant" , weights = w ) . ravel () X = np . concatenate...X , y = datasets . load_digits ( return_X_y = True ) X = np ....scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html -
Single estimator versus bagging: bias-variance ...
* 10 - 5 X = np . sort ( X ) if n_repeat == 1 : y = f ( X ) +...X_train = [] y_train = [] for i in range ( n_repeat ): X , y = generate...scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html -
GroupShuffleSplit — scikit-learn 1.5.0 document...
splits=2, random_state=42, test_size=None, train_size=0.7) >>>...n_splits = 5 , * , test_size = None , train_size = None , random_state...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
export_graphviz — scikit-learn 1.5.0 documentation
out_file = None , * , max_depth = None , feature_names = None ,..., class_names = None , label = 'all' , filled = False , leaves_parallel...scikit-learn.org/stable/modules/generated/sklearn.tree.export_graphviz.html -
Sparse inverse covariance estimation — scikit-l...
cov /= d cov /= d [:, np . newaxis ] prec *= d prec *= d [:,..., size = n_samples ) X -= X . mean ( axis = 0 ) X /= X . std...scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html -
lars_path_gram — scikit-learn 1.5.0 documentation
max_iter = 500 , alpha_min = 0 , method = 'lar' , copy_X = True ,...eps = 2.220446049250313e-16 , copy_Gram = True , verbose = 0 ,...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path_gram.html -
VotingClassifier — scikit-learn 1.5.0 documenta...
voting = 'hard' , weights = None , n_jobs = None , flatten_transform...voting=’soft’ If voting=’soft’ and flatten_transform=True, transform...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html