# Source code for evalml.pipelines.components.estimators.regressors.decision_tree_regressor

```
"""Decision Tree Regressor."""
from sklearn.tree import DecisionTreeRegressor as SKDecisionTreeRegressor
from skopt.space import Integer
from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
[docs]class DecisionTreeRegressor(Estimator):
"""Decision Tree Regressor.
Args:
criterion ({"mse", "friedman_mse", "mae", "poisson"}): The function to measure the quality of a split.
Supported criteria are:
- "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node
- "friedman_mse", which uses mean squared error with Friedman"s improvement score for potential splits
- "mae" for the mean absolute error, which minimizes the L1 loss using the median of each terminal node,
- "poisson" which uses reduction in Poisson deviance to find splits.
max_features (int, float or {"auto", "sqrt", "log2"}): The number of features to consider when looking for the best split:
- If int, then consider max_features features at each split.
- If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
- If "auto", then max_features=sqrt(n_features).
- If "sqrt", then max_features=sqrt(n_features).
- If "log2", then max_features=log2(n_features).
- If None, then max_features = n_features.
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.
max_depth (int): The maximum depth of the tree. Defaults to 6.
min_samples_split (int or float): The minimum number of samples required to split an internal node:
- If int, then consider min_samples_split as the minimum number.
- If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
Defaults to 2.
min_weight_fraction_leaf (float): The minimum weighted fraction of the sum total of weights
(of all the input samples) required to be at a leaf node. Defaults to 0.0.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
name = "Decision Tree Regressor"
hyperparameter_ranges = {
"criterion": ["mse", "friedman_mse", "mae"],
"max_features": ["auto", "sqrt", "log2"],
"max_depth": Integer(4, 10),
}
"""{
"criterion": ["mse", "friedman_mse", "mae"],
"max_features": ["auto", "sqrt", "log2"],
"max_depth": Integer(4, 10),
}"""
model_family = ModelFamily.DECISION_TREE
"""ModelFamily.DECISION_TREE"""
supported_problem_types = [
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]
"""[
ProblemTypes.REGRESSION,
ProblemTypes.TIME_SERIES_REGRESSION,
]"""
def __init__(
self,
criterion="mse",
max_features="auto",
max_depth=6,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
random_seed=0,
**kwargs,
):
parameters = {
"criterion": criterion,
"max_features": max_features,
"max_depth": max_depth,
"min_samples_split": min_samples_split,
"min_weight_fraction_leaf": min_weight_fraction_leaf,
}
parameters.update(kwargs)
dt_regressor = SKDecisionTreeRegressor(random_state=random_seed, **parameters)
super().__init__(
parameters=parameters, component_obj=dt_regressor, random_seed=random_seed
)
```