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#/sklearn/sklearn.kernel_approximation/RBFSampler/__init__/gamma

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Minimal API Data for `sklearn/sklearn.kernel_approximation/RBFSampler/__init__/gamma`

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    "package": "sklearn",
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    "modules": [
        {
            "id": "sklearn/sklearn.kernel_approximation",
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            "description": "Approximate a RBF kernel feature map using random Fourier features.\n\nIt implements a variant of Random Kitchen Sinks.[1]\n\nRead more in the :ref:`User Guide <rbf_kernel_approx>`.",
            "docstring": "Approximate a RBF kernel feature map using random Fourier features.\n\n    It implements a variant of Random Kitchen Sinks.[1]\n\n    Read more in the :ref:`User Guide <rbf_kernel_approx>`.\n\n    Parameters\n    ----------\n    gamma : float, default=1.0\n        Parameter of RBF kernel: exp(-gamma * x^2).\n\n    n_components : int, default=100\n        Number of Monte Carlo samples per original feature.\n        Equals the dimensionality of the computed feature space.\n\n    random_state : int, RandomState instance or None, default=None\n        Pseudo-random number generator to control the generation of the random\n        weights and random offset when fitting the training data.\n        Pass an int for reproducible output across multiple function calls.\n        See :term:`Glossary <random_state>`.\n\n    Attributes\n    ----------\n    random_offset_ : ndarray of shape (n_components,), dtype=float64\n        Random offset used to compute the projection in the `n_components`\n        dimensions of the feature space.\n\n    random_weights_ : ndarray of shape (n_features, n_components),        dtype=float64\n        Random projection directions drawn from the Fourier transform\n        of the RBF kernel.\n\n    n_features_in_ : int\n        Number of features seen during :term:`fit`.\n\n        .. versionadded:: 0.24\n\n    feature_names_in_ : ndarray of shape (`n_features_in_`,)\n        Names of features seen during :term:`fit`. Defined only when `X`\n        has feature names that are all strings.\n\n        .. versionadded:: 1.0\n\n    See Also\n    --------\n    AdditiveChi2Sampler : Approximate feature map for additive chi2 kernel.\n    Nystroem : Approximate a kernel map using a subset of the training data.\n    PolynomialCountSketch : Polynomial kernel approximation via Tensor Sketch.\n    SkewedChi2Sampler : Approximate feature map for\n        \"skewed chi-squared\" kernel.\n    sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.\n\n    Notes\n    -----\n    See \"Random Features for Large-Scale Kernel Machines\" by A. Rahimi and\n    Benjamin Recht.\n\n    [1] \"Weighted Sums of Random Kitchen Sinks: Replacing\n    minimization with randomization in learning\" by A. Rahimi and\n    Benjamin Recht.\n    (https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)\n\n    Examples\n    --------\n    >>> from sklearn.kernel_approximation import RBFSampler\n    >>> from sklearn.linear_model import SGDClassifier\n    >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]\n    >>> y = [0, 0, 1, 1]\n    >>> rbf_feature = RBFSampler(gamma=1, random_state=1)\n    >>> X_features = rbf_feature.fit_transform(X)\n    >>> clf = SGDClassifier(max_iter=5, tol=1e-3)\n    >>> clf.fit(X_features, y)\n    SGDClassifier(max_iter=5)\n    >>> clf.score(X_features, y)\n    1.0\n    "
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            "decorators": [],
            "parameters": [
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                    "id": "sklearn/sklearn.kernel_approximation/RBFSampler/__init__/gamma",
                    "name": "gamma",
                    "qname": "sklearn.kernel_approximation.RBFSampler.__init__.gamma",
                    "default_value": "1.0",
                    "assigned_by": "NAME_ONLY",
                    "is_public": true,
                    "docstring": {
                        "type": "float, default=1.0",
                        "description": "Parameter of RBF kernel: exp(-gamma * x^2)."
                    },
                    "type": {}
                }
            ],
            "results": [],
            "is_public": true,
            "reexported_by": [],
            "description": "",
            "docstring": ""
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}

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