val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?order:[ `Ndarray of Ndarray.t | `Random ] ->
?cv:
[ `Int of int | `CrossValGenerator of Py.Object.t | `Ndarray of Ndarray.t ] ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
base_estimator:Py.Object.t ->
unit ->
tA multi-label model that arranges regressions into a chain.
Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of models that are earlier in the chain.
Read more in the :ref:`User Guide <regressorchain>`.
Parameters ---------- base_estimator : estimator The base estimator from which the classifier chain is built.
order : array-like of shape (n_outputs,) or 'random', optional By default the order will be determined by the order of columns in the label matrix Y.::
order = 0, 1, 2, ..., Y.shape[1] - 1
The order of the chain can be explicitly set by providing a list of integers. For example, for a chain of length 5.::
order = 1, 3, 2, 4, 0
means that the first model in the chain will make predictions for column 1 in the Y matrix, the second model will make predictions for column 3, etc.
If order is 'random' a random ordering will be used.
cv : int, cross-validation generator or an iterable, optional (default=None) Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. If cv is None the true labels are used when fitting. Otherwise possible inputs for cv are:
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`.
The random number generator is used to generate random chain orders.
Attributes ---------- estimators_ : list A list of clones of base_estimator.
order_ : list The order of labels in the classifier chain.
See also -------- ClassifierChain: Equivalent for classification MultioutputRegressor: Learns each output independently rather than chaining.
val fit :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Py.Object.t ->
t ->
tFit the model to data matrix X and targets Y.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input data. Y : array-like, shape (n_samples, n_classes) The target values.
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 on the data matrix X using the ClassifierChain model.
Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) The input data.
Returns ------- Y_pred : array-like, shape (n_samples, n_classes) The predicted values.
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
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 estimators_ : t -> Py.Object.tAttribute estimators_: see constructor for documentation
val order_ : t -> Py.Object.tAttribute order_: 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.