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RobustScaler — scikit-learn 1.8.0 documen...
- 2. , 2. ], ... [ - 2. , 1. , 3. ], ... [ 4. , 1. , - 2. ]]...transform ( X ) array([[ 0. , -2. , 0. ], [-1. , 0. , 0.4], [ 1....scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html -
MultiTaskElasticNet — scikit-learn 1.8.0 ...
[ 2 , 2 ]], [[ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]]) Multi...is: ( 1 / ( 2 * n_samples )) * || Y - XW || _Fro ^ 2 + alpha *...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html -
root_mean_squared_error — scikit-learn 1....
2 , 7 ] >>> y_pred = [ 2.5 , 0.0 , 2 , 8 ] >>>...>>> y_pred = [[ 0 , 2 ],[ - 1 , 2 ],[ 8 , - 5 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html -
test.log
2024-11-13 10:00:05 INFO [worker-2] Indexing document: test.pdf 2024-11-13...2024-11-13 10:00:06 DEBUG [worker-2] Extracted text content: 吾輩は猫である。名前はまだない。...raw.githubusercontent.com/codelibs/fess-testdata/master/files/logs/test.log -
NearestNeighbors — scikit-learn 1.8.0 doc...
2 , return_distance = False ) array([[2, 0]]...) >>>...array([[2]])) As you can see, it returns [[0.5]], and [[2]], which...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
HistGradientBoostingRegressor — scikit-le...
float \(R^2\) of self.predict(X) w.r.t. y . Notes The \(R^2\) score...scikit-learn 1.2 Release Highlights for scikit-learn 1.2 Release Highlights...scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html -
incr_mean_variance_axis — scikit-learn 1....
2 , 2 ]) >>> data = np . array ([ 8 , 1 , 2 , 5...>>> scale = np . array ([ 2 , 3 , 2 ]) >>> csr = sparse...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs.incr_mean_variance_axis.html -
RBF — scikit-learn 1.8.0 documentation
x_j)^2}{2l^2} \right)\] where \(l\) is the...very smooth. See [2] , Chapter 4, Section 4.2, for further details...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html -
oas — scikit-learn 1.8.0 documentation
formula (23) states that 2/p (p being the number of features)...because for a large p, the value of 2/p is so small that it doesn’t...scikit-learn.org/stable/modules/generated/oas-function.html -
Tweedie regression on insurance claims — ...
9900 2.015718e+02 2.015412e+02 2.015342e+02 2.015600e+02...abs. error 2.730129e+02 2.722124e+02 2.740176e+02 2.731633e+02...scikit-learn.org/stable/auto_examples/linear_model/plot_tweedie_regression_insurance_claims.html