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1.4. Support Vector Machines — scikit-learn 1.7...
>>> X = [[ 0 , 0 ], [ 1 , 1 ]] >>> y = [ 0 , 1 ] >>> clf = svm...(default=’ovr’). >>> X = [[ 0 ], [ 1 ], [ 2 ], [ 3 ]] >>> Y = [ 0...scikit-learn.org/stable/modules/svm.html -
Visualization of MLP weights on MNIST — scikit-...
max_iter = 8 , alpha = 1e-4 , solver = "sgd" , verbose = 10 , random_state...return_X_y = True , as_frame = False ) X = X / 255.0 # Split data...scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html -
check_X_y — scikit-learn 1.7.1 documentation
accept_sparse = False , * , accept_large_sparse = True , dtype = 'numeric'...'numeric' , order = None , copy = False , force_writeable = False , force_all_finite...scikit-learn.org/stable/modules/generated/sklearn.utils.check_X_y.html -
partial_dependence — scikit-learn 1.7.1 documen...
sample_weight = None , categorical_features = None , feature_names = None...grid_resolution = 100 , custom_values = None , method = 'auto' , kind...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
MiniBatchKMeans — scikit-learn 1.7.1 documentation
n_clusters = 8 , * , init = 'k-means++' , max_iter = 100 , batch_size...batch_size = 1024 , verbose = 0 , compute_labels = True , random_state...scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html -
PCA — scikit-learn 1.7.1 documentation
n_components == min ( n_samples , n_features ) If n_components == 'mle'...n_components == 'mle' will interpret svd_solver == 'auto' as svd_solver...scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html -
about.rst.txt
_about: ======== About us ======== History ======= This project...contribution. Governance ========== The decision making process...scikit-learn.org/stable/_sources/about.rst.txt -
1.11. Ensembles: Gradient boosting, random fore...
min_samples_leaf = 1 , ... max_depth = 2 , ... learning_rate = 1 , ......learning_rate = 1.0 , ... max_depth = 1 , random_state = 0 ) . fit...scikit-learn.org/stable/modules/ensemble.html -
Decision boundary of semi-supervised classifier...
base_classifier = SVC ( kernel = "rbf" , gamma = 0.5 , probability = True...] y = iris . target # step size in the mesh h = 0.02 rng = np...scikit-learn.org/stable/auto_examples/semi_supervised/plot_semi_supervised_versus_svm_iris.html -
RBF — scikit-learn 1.7.1 documentation
y = load_iris ( return_X_y = True ) >>> kernel = 1.0 * RBF...( length_scale = 1.0 , length_scale_bounds = (1e-05, 100000.0)...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html