lale.lib.sklearn.decision_tree_regressor module

class lale.lib.sklearn.decision_tree_regressor.DecisionTreeRegressor(*, criterion='squared_error', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, ccp_alpha=0.0, monotonic_cst=None)

Bases: PlannedIndividualOp

Decision tree regressor from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • criterion (union type, default 'squared_error') –

    Function to measure the quality of a split.

    • ’squared_error’, ‘friedman_mse’, ‘absolute_error’, or ‘poisson’

    • or ‘mae’ or ‘mse’, not for optimizer

  • splitter (‘best’ or ‘random’, default ‘best’) – Strategy to choose the split at each node.

  • max_depth (union type, default None) –

    Maximum depth of the tree.

    • integer, >=1, >=3 for optimizer, <=5 for optimizer

    • or None

      If None, then nodes are expanded until all leaves are pure, or until all leaves contain less than min_samples_split samples.

  • min_samples_split (union type, default 2) –

    The minimum number of samples required to split an internal node.

    • integer, >=2, <=’X/maxItems’, not for optimizer

      Consider min_samples_split as the minimum number.

    • or float, >0.0, >=0.01 for optimizer, <=1.0, <=0.5 for optimizer, default 0.05

      min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

  • min_samples_leaf (union type, default 1) –

    The minimum number of samples required to be at a leaf node.

    • integer, >=1, <=’X/maxItems’, not for optimizer

      Consider min_samples_leaf as the minimum number.

    • or float, >0.0, >=0.01 for optimizer, <=0.5, default 0.05

      min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

  • min_weight_fraction_leaf (float, >=0.0, <=0.5, optional, not for optimizer, default 0.0) – Minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.

  • max_features (union type, default None) –

    The number of features to consider when looking for the best split.

    • integer, >=2, <=’X/items/maxItems’, not for optimizer

      Consider max_features features at each split.

    • or float, >0.0, >=0.01 for optimizer, <=1.0, uniform distribution, default 0.5

      max_features is a fraction and int(max_features * n_features) features are considered at each split.

    • or ‘sqrt’, ‘log2’, or None

  • random_state (union type, optional, not for optimizer, default None) –

    Seed of pseudo-random number generator.

    • numpy.random.RandomState

    • or None

      RandomState used by np.random

    • or integer

      Explicit seed.

  • max_leaf_nodes (union type, optional, not for optimizer, default None) –

    Grow a tree with max_leaf_nodes in best-first fashion.

    • integer, >=1, >=3 for optimizer, <=1000 for optimizer

    • or None

      Unlimited number of leaf nodes.

  • min_impurity_decrease (float, >=0.0, <=10.0 for optimizer, optional, not for optimizer, default 0.0) – A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  • ccp_alpha (float, >=0.0, <=0.1 for optimizer, optional, not for optimizer, 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.

  • monotonic_cst (union type, optional, not for optimizer, default None) –

    Indicates the monotonicity constraint to enforce on each feature. Monotonicity constraints are not supported for: multioutput regressions (i.e. when n_outputs > 1),

    regressions trained on data with missing values.

    • array of items : -1, 0, or 1

      array-like of int of shape (n_features)

    • or None

      No constraints are applied.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array) –

    The outer array is over samples aka rows.

    • items : array of items : float

      The inner array is over features aka columns.

  • y (array of items : float) – The target values (real numbers).

  • sample_weight (union type, optional) –

    Sample weights.

    • array of items : float

    • or None

      Samples are equally weighted.

  • check_input (boolean, optional, default True) – Allow to bypass several input checking.

  • X_idx_sorted (union type, optional, default None) –

    The indexes of the sorted training input samples. If many tree

    • array of items : array of items : float

    • or None

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

  • X (array, optional) –

    The outer array is over samples aka rows.

    • items : array of items : float

      The inner array is over features aka columns.

  • check_input (boolean, optional, default True) – Allow to bypass several input checking.

Returns

result – The predicted classes, or the predict values.

Return type

array of items : float