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StratifiedShuffleSplit — scikit-learn 1.7.2 doc...
1 , 2 ], [ 3 , 4 ]]) >>> y = np . array ([ 0 , 0 , 0 , 1 , 1 ,...np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ], [ 1...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html -
FunctionTransformer — scikit-learn 1.7.2 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 -
KernelDensity — scikit-learn 1.7.2 documentation
algorithm {‘kd_tree’, ‘ball_tree’, ‘auto’}, default=’auto’ The tree...to use. kernel {‘gaussian’, ‘tophat’, ‘epanechnikov’, ‘exponential’,...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html -
PowerTransformer — scikit-learn 1.7.2 documenta...
[[-1.316 -0.707] [ 0.209 -0.707] [ 1.106 1.414]] fit ( X , y =...generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"] . If input_features...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html -
GroupShuffleSplit — scikit-learn 1.7.2 document...
= ( 8 , 1 )) >>> groups = np . array ([ 1 , 1 , 2 , 2 , 2 , 3...gss . split ( X , y , groups )): ... print ( f "Fold { i } :" )...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
TimeSeriesSplit — scikit-learn 1.7.2 documentation
enumerate ( tscv . split ( X )): ... print ( f "Fold { i } :" ) ... print...tscv . split ( X )): ... print ( f "Fold { i } :" ) ... print...scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html -
Gaussian Process for Machine Learning — scikit-...
module. Ability of Gaussian process regression (GPR) to estimate...noise-level Ability of Gaussian process regression (GPR) to estimate...scikit-learn.org/stable/auto_examples/gaussian_process/index.html -
Working with text documents — scikit-learn 1.7....
documents # Examples concerning the sklearn.feature_extraction.text...documents using k-means Clustering text documents using k-means FeatureHasher...scikit-learn.org/stable/auto_examples/text/index.html -
1.17. Neural network models (supervised) — scik...
clf . predict ([[ 2. , 2. ], [ - 1. , - 2. ]]) array([1, 0]) MLP...>>> X = [[ 0. , 0. ], [ 1. , 1. ]] >>> y = [ 0 , 1 ] >>> clf =...scikit-learn.org/stable/modules/neural_networks_supervised.html -
auto_examples_jupyter.zip
floor((x - orig) / dx).astype(np.int64)\n alpha = (x - orig - floor_x...{ "cell_type": "markdown", "metadata": {}, "source": [ "\n# Compressive...scikit-learn.org/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip