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@@ -400,7 +400,7 @@
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},
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{
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"name": "sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
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- "description": "Linear Discriminant Analysis\n\nA classifier with a linear decision boundary, generated by fitting class\nconditional densities to the data and using Bayes' rule.\n\nThe model fits a Gaussian density to each class, assuming that all classes\nshare the same covariance matrix.\n\nThe fitted model can also be used to reduce the dimensionality of the input\nby projecting it to the most discriminative directions, using the\n`transform` method.\n\n.. versionadded:: 0.17\n*LinearDiscriminantAnalysis*.\n\nRead more in the :ref:`User Guide <lda_qda>`.\n",
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+ "description": "Linear Discriminant Analysis.\n\nA classifier with a linear decision boundary, generated by fitting class\nconditional densities to the data and using Bayes' rule.\n\nThe model fits a Gaussian density to each class, assuming that all classes\nshare the same covariance matrix.\n\nThe fitted model can also be used to reduce the dimensionality of the input\nby projecting it to the most discriminative directions, using the\n`transform` method.\n\n.. versionadded:: 0.17\n*LinearDiscriminantAnalysis*.\n\nRead more in the :ref:`User Guide <lda_qda>`.\n",
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"attributes": [
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{
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"default": "svd",
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@@ -1093,7 +1093,7 @@
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},
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{
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"default": true,
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- "description": "This parameter is ignored when ``fit_intercept`` is set to False.\nIf True, the regressors X will be normalized before regression by\nsubtracting the mean and dividing by the l2-norm.\nIf you wish to standardize, please use\n:class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``\non an estimator with ``normalize=False``.\n",
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+ "description": "This parameter is ignored when ``fit_intercept`` is set to False.\nIf True, the regressors X will be normalized before regression by\nsubtracting the mean and dividing by the l2-norm.\nIf you wish to standardize, please use\n:class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``\non an estimator with ``normalize=False``.\n\n.. deprecated:: 1.0\n``normalize`` was deprecated in version 1.0. It will default\nto False in 1.2 and be removed in 1.4.\n",
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"name": "normalize",
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"option": "optional",
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"type": "boolean"
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@@ -1463,7 +1463,7 @@
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},
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{
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"default": "uniform",
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- "description": "weight function used in prediction. Possible values:\n\n- 'uniform' : uniform weights. All points in each neighborhood\nare weighted equally.\n- 'distance' : weight points by the inverse of their distance.\nin this case, closer neighbors of a query point will have a\ngreater influence than neighbors which are further away.\n- [callable] : a user-defined function which accepts an\narray of distances, and returns an array of the same shape\ncontaining the weights.\n",
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+ "description": "Weight function used in prediction. Possible values:\n\n- 'uniform' : uniform weights. All points in each neighborhood\nare weighted equally.\n- 'distance' : weight points by the inverse of their distance.\nin this case, closer neighbors of a query point will have a\ngreater influence than neighbors which are further away.\n- [callable] : a user-defined function which accepts an\narray of distances, and returns an array of the same shape\ncontaining the weights.\n",
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"name": "weights",
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"option": "optional"
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},
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@@ -1489,7 +1489,7 @@
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},
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{
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"default": "minkowski",
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- "description": "the distance metric to use for the tree. The default metric is\nminkowski, and with p=2 is equivalent to the standard Euclidean\nmetric. See the documentation of :class:`DistanceMetric` for a\nlist of available metrics.\nIf metric is \"precomputed\", X is assumed to be a distance matrix and\nmust be square during fit. X may be a :term:`sparse graph`,\nin which case only \"nonzero\" elements may be considered neighbors.\n",
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+ "description": "The distance metric to use for the tree. The default metric is\nminkowski, and with p=2 is equivalent to the standard Euclidean\nmetric. See the documentation of :class:`DistanceMetric` for a\nlist of available metrics.\nIf metric is \"precomputed\", X is assumed to be a distance matrix and\nmust be square during fit. X may be a :term:`sparse graph`,\nin which case only \"nonzero\" elements may be considered neighbors.\n",
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"name": "metric"
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},
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{
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@@ -1729,7 +1729,7 @@
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},
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{
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"name": "sklearn.preprocessing._data.StandardScaler",
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- "description": "Standardize features by removing the mean and scaling to unit variance\n\nThe standard score of a sample `x` is calculated as:\n\nz = (x - u) / s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 and have variance in the same\norder. If a feature has a variance that is orders of magnitude larger\nthat others, it might dominate the objective function and make the\nestimator unable to learn from other features correctly as expected.\n\nThis scaler can also be applied to sparse CSR or CSC matrices by passing\n`with_mean=False` to avoid breaking the sparsity structure of the data.\n\nRead more in the :ref:`User Guide <preprocessing_scaler>`.\n",
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+ "description": "Standardize features by removing the mean and scaling to unit variance.\n\nThe standard score of a sample `x` is calculated as:\n\nz = (x - u) / s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 and have variance in the same\norder. If a feature has a variance that is orders of magnitude larger\nthat others, it might dominate the objective function and make the\nestimator unable to learn from other features correctly as expected.\n\nThis scaler can also be applied to sparse CSR or CSC matrices by passing\n`with_mean=False` to avoid breaking the sparsity structure of the data.\n\nRead more in the :ref:`User Guide <preprocessing_scaler>`.\n",
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"attributes": [
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{
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"default": true,
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@@ -1788,7 +1788,7 @@
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"attributes": [
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{
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"default": true,
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- "description": "set to False to perform inplace binarization and avoid a copy (if\nthe input is already a numpy array or a scipy.sparse CSR matrix).\n",
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+ "description": "Set to False to perform inplace binarization and avoid a copy (if\nthe input is already a numpy array or a scipy.sparse CSR matrix).\n",
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"name": "copy",
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"option": "optional",
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"type": "boolean"
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Lutz Roeder