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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 -
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 -
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 -
sklearn.metrics.make_scorer — scikit-learn 1.4....
param_grid = { 'C' : [ 1 , 10 ]}, ... scoring =...scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html -
sklearn.svm.OneClassSVM — scikit-learn 1.4.2 do...
Rescale C per sample. Higher weights force...estimator. Notes If X is not a C-ordered contiguous array it is...scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html -
sklearn.model_selection.validation_curve — scik...
param_range = "C" , np . logspace ( - 8 , 3 , 10...scikit-learn.org/stable/modules/generated/sklearn.model_selection.validation_curve.html -
neighbors.rst.txt
:math:`C`, then we know that points :math:`A` and :math:`C` are...p_{i}=\sum\limits_{j \in C_i}{p_{i j}} where :math:`C_i` is the set of...scikit-learn.org/stable/_sources/modules/neighbors.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 -
sklearn.cluster.KMeans — scikit-learn 1.4.2 doc...
that if the original data is not C-contiguous, a copy will be made...the data will be converted to C ordering, which will cause a memory...scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html -
Putting it all together — scikit-learn 1.4.2 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