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sklearn.manifold.Isomap — scikit-learn 1.4.2 do...
frobenius_norm[K(D) - K(D_fit)] / n_samples Where D is the matrix...isomap kernel: K(D) = -0.5 * (I - 1/n_samples) * D^2 * (I - 1/n_samples)...scikit-learn.org/stable/modules/generated/sklearn.manifold.Isomap.html -
1.7. Gaussian Processes — scikit-learn 1.4.2 do...
coefficients of \(x_d (d = 1, . . . , D)\) and a prior of \(N(0,...\text{exp}\left(- \frac{d(x_i, x_j)^2}{2l^2} \right)\] where \(d(\cdot, \cdot)\)...scikit-learn.org/stable/modules/gaussian_process.html -
4.2. Permutation feature importance — scikit-le...
dataset (training or validation) \(D\) . Compute the reference score...of the model \(m\) on data \(D\) (for instance the accuracy for...scikit-learn.org/stable/modules/permutation_importance.html -
sklearn.datasets.make_sparse_coded_signal — sci...
such that D is of shape (n_features, n_components)...default=False By default, Y, D and X are not transposed. New...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_coded_signal.html -
sklearn.feature_extraction.DictVectorizer — sci...
DictVectorizer ( sparse = False ) >>> D = [{ 'foo' : 1 , 'bar' : 2 },...}] >>> X = v . fit_transform ( D ) >>> X array([[2., 0., 1.], [0.,...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html -
sklearn.gaussian_process.kernels.Matern — sciki...
\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)\] where \(d(\cdot,\cdot)\)...\Bigg( \frac{\sqrt{2\nu}}{l} d(x_i , x_j ) \Bigg)^\nu K_\nu\Bigg(...scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.Matern.html -
sklearn.metrics.pairwise_distances — scikit-lea...
n_samples_Y) A distance matrix D such that D_{i, j} is the distance between...for usage examples. Returns : D ndarray of shape (n_samples_X,...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html -
design-style.1e8bd061cd6da7fc9cf755528e8ffc24.m...
sd-d-none{display:none !important}.sd-d-inline{display:inline...!important}.sd-d-inline-block{display:inline-block !important}.sd-d-block{display:block...scikit-learn.org/dev/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css -
1.10. Decision Trees — scikit-learn 1.4.2 docum...
dataset \(D\) is defined as follows: \[\mathrm{LL}(D, T) = -\frac{1}{n}...y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i))\] where \(D\) is a...scikit-learn.org/stable/modules/tree.html -
decomposition.rst.txt
:math:`d \in D`, draw the topic proportions :math:`\theta_d \sim...definitions of :math:`d_{KL}` and :math:`d_{IS}` respectively....scikit-learn.org/stable/_sources/modules/decomposition.rst.txt