snowflake.ml.modeling.tree.DecisionTreeClassifier

class snowflake.ml.modeling.tree.DecisionTreeClassifier(*, criterion='gini', 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, class_weight=None, ccp_alpha=0.0, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)

Bases: BaseTransformer

A decision tree classifier For more details on this class, see sklearn.tree.DecisionTreeClassifier

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

passthrough_cols: Optional[Union[str, List[str]]]

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

criterion: {“gini”, “entropy”, “log_loss”}, default=”gini”

The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see tree_mathematical_formulation.

splitter: {“best”, “random”}, default=”best”

The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.

max_depth: int, default=None

The maximum depth of the tree. 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: int or float, default=2

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.

min_samples_leaf: int or float, 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.

  • If int, then consider min_samples_leaf as the minimum number.

  • If float, then 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, default=0.0

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_features: int, float or {“auto”, “sqrt”, “log2”}, default=None

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 max(1, int(max_features * n_features_in_)) features are considered at each split.

  • If “sqrt”, then max_features=sqrt(n_features).

  • If “log2”, then max_features=log2(n_features).

  • If None, then max_features=n_features.

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.

random_state: int, RandomState instance or None, default=None

Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See Glossary for details.

max_leaf_nodes: int, default=None

Grow a tree 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.

min_impurity_decrease: float, default=0.0

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
                    - N_t_L / N_t * left_impurity)
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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.

class_weight: dict, list of dict or “balanced”, default=None

Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

For multi-output, the weights of each column of y will be multiplied.

Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

ccp_alpha: non-negative float, 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 minimal_cost_complexity_pruning for details.

Base class for all transformers.

Methods

fit(dataset: Union[DataFrame, DataFrame]) → DecisionTreeClassifier

Build a decision tree classifier from the training set (X, y) For more details on this function, see sklearn.tree.DecisionTreeClassifier.fit

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

self

fit_transform(dataset: Union[DataFrame, DataFrame]) → Union[Any, ndarray[Any, dtype[Any]]]
Returns:

Transformed dataset.

get_input_cols() → List[str]

Input columns getter.

Returns:

Input columns.

get_label_cols() → List[str]

Label column getter.

Returns:

Label column(s).

get_output_cols() → List[str]

Output columns getter.

Returns:

Output columns.

get_params(deep: bool = True) → Dict[str, Any]

Get parameters for this transformer.

Args:
deep: If True, will return the parameters for this transformer and

contained subobjects that are transformers.

Returns:

Parameter names mapped to their values.

get_passthrough_cols() → List[str]

Passthrough columns getter.

Returns:

Passthrough column(s).

get_sample_weight_col() → Optional[str]

Sample weight column getter.

Returns:

Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) → Dict[str, Any]

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Args:
default_sklearn_obj: Sklearn object used to get default parameter values. Necessary when

sklearn_added_keyword_to_version_dict is provided.

sklearn_initial_keywords: Initial keywords in sklearn. sklearn_unused_keywords: Sklearn keywords that are unused in snowml. snowml_only_keywords: snowml only keywords not present in sklearn. sklearn_added_keyword_to_version_dict: Added keywords mapped to the sklearn versions in which they were

added.

sklearn_added_kwarg_value_to_version_dict: Added keyword argument values mapped to the sklearn versions

in which they were added.

sklearn_deprecated_keyword_to_version_dict: Deprecated keywords mapped to the sklearn versions in which

they were deprecated.

sklearn_removed_keyword_to_version_dict: Removed keywords mapped to the sklearn versions in which they

were removed.

Returns:

Sklearn parameter names mapped to their values.

predict(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]

Predict class or regression value for X For more details on this function, see sklearn.tree.DecisionTreeClassifier.predict

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Transformed dataset.

predict_log_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_log_proba_') → Union[DataFrame, DataFrame]

Predict class probabilities of the input samples X For more details on this function, see sklearn.tree.DecisionTreeClassifier.predict_proba

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

output_cols_prefix: str

Prefix for the response columns

Returns:

Output dataset with log probability of the sample for each class in the model.

predict_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_proba_') → Union[DataFrame, DataFrame]

Predict class probabilities of the input samples X For more details on this function, see sklearn.tree.DecisionTreeClassifier.predict_proba

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns

Returns:

Output dataset with probability of the sample for each class in the model.

score(dataset: Union[DataFrame, DataFrame]) → float

Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.tree.DecisionTreeClassifier.score

Raises:

TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Args:
dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]

Snowpark or Pandas DataFrame.

Returns:

Score.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None
set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → DecisionTreeClassifier

Input columns setter.

Args:

input_cols: A single input column or multiple input columns.

Returns:

self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) → Base

Label column setter.

Args:

label_cols: A single label column or multiple label columns if multi task learning.

Returns:

self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) → Base

Output columns setter.

Args:

output_cols: A single output column or multiple output columns.

Returns:

self

set_params(**params: Dict[str, Any]) → None

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Args:

**params: Transformer parameter names mapped to their values.

Raises:

SnowflakeMLException: Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) → Base

Passthrough columns setter.

Args:
passthrough_cols: Column(s) that should not be used or modified by the estimator/transformer.

Estimator/Transformer just passthrough these columns without any modifications.

Returns:

self

set_sample_weight_col(sample_weight_col: Optional[str]) → Base

Sample weight column setter.

Args:

sample_weight_col: A single column that represents sample weight.

Returns:

self

to_sklearn() → Any

Get sklearn.tree.DecisionTreeClassifier object.

Attributes

model_signatures

Returns model signature of current class.

Raises:

exceptions.SnowflakeMLException: If estimator is not fitted, then model signature cannot be inferred

Returns:

Dict[str, ModelSignature]: each method and its input output signature

Sprache: Deutsch