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cross_validation.rst.txt
"c", "c", "c", "d", "d", "d"] >>> groups...= ["a", "b", "b", "b", "c", "c", "c", "a"] >>> groups = [1, 1,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
2.4. Biclustering — scikit-learn 1.5.0 document...
as follows: \[A_n = R^{-1/2} A C^{-1/2}\] Where \(R\) is the diagonal...to \(\sum_{j} A_{ij}\) and \(C\) is the diagonal matrix with...scikit-learn.org/stable/modules/biclustering.html -
1.3. Kernel ridge regression — scikit-learn 1.5...
parameters \(\epsilon\) and \(C\) of the SVR ; \(\epsilon = 0\)...scikit-learn.org/stable/modules/kernel_ridge.html -
decomposition.rst.txt
C. Sorensen, and C. Yang, (1998) .. _LSA:....org/Boutsidis_PRE_08.pdf>`_ C. Boutsidis, E. Gallopoulos, 2008...scikit-learn.org/stable/_sources/modules/decomposition.rst.txt -
6.6. Random Projection — scikit-learn 1.5.0 doc...
\[\begin{split}\left\{ \begin{array}{c c l} -\sqrt{\frac{s}{n_{\text{components}}}}...scikit-learn.org/stable/modules/random_projection.html -
preprocessing.rst.txt
"b" and "c" are their own categories, unknown...... [["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3 + [np.nan]],...scikit-learn.org/stable/_sources/modules/preprocessing.rst.txt -
linear_model.rst.txt
``C`` is given by ``alpha = 1 / C`` or ``alpha =...:func:`sklearn.svm.l1_min_c` allows to calculate the lower bound for C in order...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
feature_selection.rst.txt
the parameter C controls the sparsity: the smaller C the fewer features...(150, 4) >>> lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
An introduction to machine learning with scikit...
C = 100. ) The clf (for classifier)...digits . target [: - 1 ]) SVC(C=100.0, gamma=0.001) Now you can...scikit-learn.org/stable/tutorial/basic/tutorial.html -
Putting it all together — scikit-learn 1.5.0 do...
"logistic__C" : np . logspace ( - 4 , 4 , 4...t0 = time () param_grid = { "C" : loguniform ( 1e3 , 1e5 ), "gamma"...scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html