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SparseCoder — scikit-learn 1.7.1 documentation
[ 1 , 1 , 1 ], ... [ 0 , 1 , 1 ], ... [ 0 , 2 , 1 ]], ... dtype...np . array ( ... [[ 0 , 1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html -
validation_curve — scikit-learn 1.7.1 documenta...
instead. E.g.: validation_curve(..., params={'groups': groups})...instance (e.g., GroupKFold ). Changed in version 1.6: groups can...scikit-learn.org/stable/modules/generated/sklearn.model_selection.validation_curve.html -
recall_score — scikit-learn 1.7.1 documentation
weights. zero_division {“warn”, 0.0, 1.0, np.nan}, default=”warn”...average {‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None,...scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html -
mean_absolute_percentage_error — scikit-learn 1...
= [ 3 , - 0.5 , 2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...[ - 1 , 1 ], [ 7 , - 6 ]] >>> y_pred = [[ 0 , 2 ], [ - 1 , 2...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html -
LinearRegression — scikit-learn 1.7.1 documenta...
np . array ([[ 1 , 1 ], [ 1 , 2 ], [ 2 , 2 ], [ 2 , 3 ]]) >>>...>>> # y = 1 * x_0 + 2 * x_1 + 3 >>> y = np . dot ( X , np . array...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html -
class_likelihood_ratios — scikit-learn 1.7.1 do...
class_likelihood_ratios ([ 0 , 1 , 0 , 1 , 0 ], [ 1 , 1 , 0 , 0 , 0 ]) (1.5,...{"LR+": 1.0, "LR-": 1.0} a dict in the format {"LR+": value_1, "LR-":...scikit-learn.org/stable/modules/generated/sklearn.metrics.class_likelihood_ratios.html -
Kernel Approximation — scikit-learn 1.7.1 docum...
top Ctrl + K GitHub Choose version Kernel Approximation # Examples...concerning the sklearn.kernel_approximation module. Scalable learning...scikit-learn.org/stable/auto_examples/kernel_approximation/index.html -
OrdinalEncoder — scikit-learn 1.7.1 documentation
([[ 1 , 0 ], [ 0 , 1 ]]) array([['Male', 1], ['Female', 2]], dtype=object)...]]) array([[0., 2.], [1., 0.]]) >>> enc . inverse_transform ([[...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html -
FunctionTransformer — scikit-learn 1.7.1 docume...
]]) >>> transformer . transform ( X ) array([[0. , 0.6931], [1.0986,...[1.0986, 1.3862]]) fit ( X , y = None ) [source] # Fit transformer...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html -
Ridge — scikit-learn 1.7.1 documentation
solver {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’,...sparse data. However, only ‘lsqr’, ‘sag’, ‘sparse_cg’, and ‘lbfgs’...scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html