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module mlforecast.distributed.models.dask.xgb

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class DaskXGBForecast

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property best_iteration

The best iteration obtained by early stopping. This attribute is 0-based, for instance if the best iteration is the first round, then best_iteration is 0.

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property best_score

The best score obtained by early stopping.

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property client

The dask client used in this model. The Client object can not be serialized for transmission, so if task is launched from a worker instead of directly from the client process, this attribute needs to be set at that worker.

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property coef_

Coefficients property .. note:: Coefficients are defined only for linear learners Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree). Returns ------- coef_ : array of shape [n_features] or [n_classes, n_features]

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property feature_importances_

Feature importances property, return depends on importance_type parameter. When model trained with multi-class/multi-label/multi-target dataset, the feature importance is “averaged” over all targets. The “average” is defined based on the importance type. For instance, if the importance type is “total_gain”, then the score is sum of loss change for each split from all trees. Returns ------- feature_importances_ : array of shape [n_features] except for multi-class linear model, which returns an array with shape (n_features, n_classes)

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property feature_names_in_

Names of features seen during :py:meth:fit. Defined only when X has feature names that are all strings.

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property intercept_

Intercept (bias) property For tree-based model, the returned value is the base_score. Returns ------- intercept_ : array of shape (1,) or [n_classes]

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property model_

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property n_features_in_

Number of features seen during :py:meth:fit.