val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?loss:[ `Deviance | `Exponential ] ->
?learning_rate:float ->
?n_estimators:int ->
?subsample:float ->
?criterion:string ->
?min_samples_split:[ `Int of int | `Float of float ] ->
?min_samples_leaf:[ `Int of int | `Float of float ] ->
?min_weight_fraction_leaf:float ->
?max_depth:int ->
?min_impurity_decrease:float ->
?min_impurity_split:float ->
?init:[ `Estimator of Py.Object.t | `Zero ] ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?max_features:[ `Int of int | `Float of float | `String of string | `None ] ->
?verbose:int ->
?max_leaf_nodes:[ `Int of int | `None ] ->
?warm_start:bool ->
?presort:Py.Object.t ->
?validation_fraction:float ->
?n_iter_no_change:int ->
?tol:float ->
?ccp_alpha:float ->
unit ->
tGradient Boosting for classification.
GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced.
Read more in the :ref:`User Guide <gradient_boosting>`.
Parameters ---------- loss : 'deviance', 'exponential', optional (default='deviance') loss function to be optimized. 'deviance' refers to deviance (= logistic regression) for classification with probabilistic outputs. For loss 'exponential' gradient boosting recovers the AdaBoost algorithm.
learning_rate : float, optional (default=0.1) learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators.
n_estimators : int (default=100) The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.
subsample : float, optional (default=1.0) The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias.
criterion : string, optional (default="friedman_mse") The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "mse" for mean squared error, and "mae" for the mean absolute error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases.
.. versionadded:: 0.18
min_samples_split : int, float, optional (default=2) The minimum number of samples required to split an internal node:
.. versionchanged:: 0.18 Added float values for fractions.
min_samples_leaf : int, float, optional (default=1) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.
.. versionchanged:: 0.18 Added float values for fractions.
min_weight_fraction_leaf : float, optional (default=0.) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
max_depth : integer, optional (default=3) maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables.
min_impurity_decrease : float, optional (default=0.) A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child.
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed.
.. versionadded:: 0.19
min_impurity_split : float, (default=1e-7) Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
.. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead.
init : estimator or 'zero', optional (default=None) An estimator object that is used to compute the initial predictions. ``init`` has to provide :meth:`fit` and :meth:`predict_proba`. If 'zero', the initial raw predictions are set to zero. By default, a ``DummyEstimator`` predicting the classes priors is used.
random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.
max_features : int, float, string or None, optional (default=None) The number of features to consider when looking for the best split:
Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features.
verbose : int, default: 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree.
max_leaf_nodes : int or None, optional (default=None) Grow trees with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.
warm_start : bool, default: False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. See :term:`the Glossary <warm_start>`.
presort : deprecated, default='deprecated' This parameter is deprecated and will be removed in v0.24.
.. deprecated :: 0.22
validation_fraction : float, optional, default 0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if ``n_iter_no_change`` is set to an integer.
.. versionadded:: 0.20
n_iter_no_change : int, default None ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. The split is stratified.
.. versionadded:: 0.20
tol : float, optional, default 1e-4 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops.
.. versionadded:: 0.20
ccp_alpha : non-negative float, optional (default=0.0) Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details.
.. versionadded:: 0.22
Attributes ---------- n_estimators_ : int The number of estimators as selected by early stopping (if ``n_iter_no_change`` is specified). Otherwise it is set to ``n_estimators``.
.. versionadded:: 0.20
feature_importances_ : array, shape (n_features,) The feature importances (the higher, the more important the feature).
oob_improvement_ : array, shape (n_estimators,) The improvement in loss (= deviance) on the out-of-bag samples relative to the previous iteration. ``oob_improvement_0`` is the improvement in loss of the first stage over the ``init`` estimator. Only available if ``subsample < 1.0``
train_score_ : array, shape (n_estimators,) The i-th score ``train_score_i`` is the deviance (= loss) of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the deviance on the training data.
loss_ : LossFunction The concrete ``LossFunction`` object.
init_ : estimator The estimator that provides the initial predictions. Set via the ``init`` argument or ``loss.init_estimator``.
estimators_ : ndarray of DecisionTreeRegressor,shape (n_estimators, ``loss_.K``) The collection of fitted sub-estimators. ``loss_.K`` is 1 for binary classification, otherwise n_classes.
classes_ : array of shape (n_classes,) The classes labels.
Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed.
See also -------- sklearn.ensemble.HistGradientBoostingClassifier, sklearn.tree.DecisionTreeClassifier, RandomForestClassifier AdaBoostClassifier
References ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001.
J. Friedman, Stochastic Gradient Boosting, 1999
T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009.
val get_item : index:Py.Object.t -> t -> Py.Object.tReturn the index'th estimator in the ensemble.
val apply :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tApply trees in the ensemble to X, return leaf indices.
.. versionadded:: 0.17
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, its dtype will be converted to ``dtype=np.float32``. If a sparse matrix is provided, it will be converted to a sparse ``csr_matrix``.
Returns ------- X_leaves : array-like, shape (n_samples, n_estimators, n_classes) For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. In the case of binary classification n_classes is 1.
val decision_function :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tCompute the decision function of ``X``.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- score : array, shape (n_samples, n_classes) or (n_samples,) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification produce an array of shape n_samples.
val fit :
?sample_weight:[ `Ndarray of Ndarray.t | `None ] ->
?monitor:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
t ->
tFit the gradient boosting model.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
y : array-like, shape (n_samples,) Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes.
sample_weight : array-like, shape (n_samples,) or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
monitor : callable, optional The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of ``_fit_stages`` as keyword arguments ``callable(i, self, locals())``. If the callable returns ``True`` the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshoting.
Returns ------- self : object
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict class for X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- y : array, shape (n_samples,) The predicted values.
val predict_log_proba :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict class log-probabilities for X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Raises ------ AttributeError If the ``loss`` does not support probabilities.
Returns ------- p : array, shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.
val predict_proba :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict class probabilities for X.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Raises ------ AttributeError If the ``loss`` does not support probabilities.
Returns ------- p : array, shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val staged_decision_function :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Py.Object.tCompute decision function of ``X`` for each iteration.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- score : generator of array, shape (n_samples, k) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``.
val staged_predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Py.Object.tPredict class at each stage for X.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- y : generator of array of shape (n_samples,) The predicted value of the input samples.
val staged_predict_proba :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Py.Object.tPredict class probabilities at each stage for X.
This method allows monitoring (i.e. determine error on testing set) after each stage.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``.
Returns ------- y : generator of array of shape (n_samples,) The predicted value of the input samples.
val n_estimators_ : t -> intAttribute n_estimators_: see constructor for documentation
Attribute feature_importances_: see constructor for documentation
val loss_ : t -> Py.Object.tAttribute loss_: see constructor for documentation
val init_ : t -> Py.Object.tAttribute init_: see constructor for documentation
val estimators_ : t -> Py.Object.tAttribute estimators_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.