- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 161 - 170 of 1,998 for = (0.33 sec)
-
SGDRegressor — scikit-learn 1.7.0 documentation
tol = 0.001 , shuffle = True , verbose = 0 , epsilon = 0.1 , random_state...( loss = 'squared_error' , * , penalty = 'l2' , alpha = 0.0001...scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html -
spectral_clustering — scikit-learn 1.7.0 docume...
n_clusters = 8 , n_components = None , eigen_solver = None , random_state...random_state = None , n_init = 10 , eigen_tol = 'auto' , assign_labels...scikit-learn.org/stable/modules/generated/sklearn.cluster.spectral_clustering.html -
make_classification — scikit-learn 1.7.0 docume...
n_clusters_per_class = 2 , weights = None , flip_y = 0.01 , class_sep = 1.0 ,...hypercube = True , shift = 0.0 , scale = 1.0 , shuffle = True ,...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html -
Robust covariance estimation and Mahalanobis di...
7 ) n_samples = 125 n_outliers = 25 n_features = 2 # generate...gen_cov = np . eye ( n_features ) gen_cov [ 0 , 0 ] = 2.0 X = np ....scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html -
check_scoring — scikit-learn 1.7.0 documentation
estimator = None , scoring = None , * , allow_none = False , raise_exc...X , y = load_iris ( return_X_y = True ) >>> classifier = DecisionTreeClassifi...scikit-learn.org/stable/modules/generated/sklearn.metrics.check_scoring.html -
digg-favicon.png
PixelInterleaved width=16, height=16, bitDepth=8, colorType=GrayAlpha, ...whitePointX=31269, whitePointY=32899, redX=63999, redY=33001, greenX=30000,...cdn.digg.com/static/images/digg-favicon.png -
Decision Tree Regression with AdaBoost — scikit...
color = colors [ 1 ], label = "n_estimators=1" , linewidth = 2 )...regr_1 = DecisionTreeRegresso ( max_depth = 4 ) regr_2 = AdaBoostRegressor...scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html -
The Johnson-Lindenstrauss bound for embedding w...
samples pairs nonzero = dists != 0 dists = dists [ nonzero ] for...: t0 = time () rp = SparseRandomProjecti ( n_components = n_components...scikit-learn.org/stable/auto_examples/miscellaneous/plot_johnson_lindenstrauss_bound.html -
SVM: Maximum margin separating hyperplane — sci...
y = make_blobs ( n_samples = 40 , centers = 2 , random_state...purposes clf = svm . SVC ( kernel = "linear" , C = 1000 ) clf ....scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html -
feature_extraction.rst.txt
_feature_extraction: ========== Feature extraction ========== .. currentmodule::...Loading features from dicts ========== The class :class:`DictVectorizer`...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt
