sklearn.ensemble¶

sklearn.ensemble.RandomForestClassifier¶

sklearn.ensemble.RandomForestClassifier

This class provides Random Forest Classifier, an ensemble learning model, for distributed large-scale learning.

Important

random_state value is ignored when running on a multi-node cluster.

Methods¶

sklearn.ensemble.RandomForestClassifier.fit¶

sklearn.ensemble.RandomForestClassifier.fit(X, y, sample_weight=None)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix. - y: NumPy Array - sample_weight: Numeric NumPy Array (only if data is not distributed)

sklearn.ensemble.RandomForestClassifier.predict¶

sklearn.ensemble.RandomForestClassifier.predict(X)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix.

sklearn.ensemble.RandomForestClassifier.predict_log_proba¶

sklearn.ensemble.RandomForestClassifier.predict_log_proba(X)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix.

sklearn.ensemble.RandomForestClassifier.predict_proba¶

sklearn.ensemble.RandomForestClassifier.predict_proba(X)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix.

sklearn.ensemble.RandomForestClassifier.score¶

sklearn.ensemble.RandomForestClassifier.score(X, y, sample_weight=None)

Supported Arguments

- X: NumPy Array or Pandas Dataframes. - y: NumPy Array - sample_weight: Numeric NumPy Array

Example Usage¶

>>> import bodo
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> @bodo.jit
>>> def test_random_forest_classifier(X, y):
...   clf = RandomForestClassifier(max_depth=2)
...   clf.fit(X, y)
...   ans = clf.predict(np.array([[0, 0, 0, 0]]))
...   print(ans)
...
>>> test_random_forest_classifier(X, y)
[1]

sklearn.ensemble.RandomForestRegressor¶

sklearn.ensemble.RandomForestRegressor

This class provides Random Forest Regressor, an ensemble learning model, for distributed large-scale learning.

Important

random_state value is ignored when running on a multi-node cluster.

Methods¶

sklearn.ensemble.RandomForestRegressor.fit¶

sklearn.ensemble.RandomForestRegressor.fit(X, y, sample_weight=None)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix. - y: NumPy Array - sample_weight: Numeric NumPy Array (only if data is not distributed)

sklearn.ensemble.RandomForestRegressor.predict¶

sklearn.ensemble.RandomForestRegressor.predict(X)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix.

sklearn.ensemble.RandomForestRegressor.score¶

sklearn.ensemble.RandomForestRegressor.score(X, y, sample_weight=None)

Supported Arguments

- X: NumPy Array, Pandas Dataframes, or CSR sparse matrix. - y: NumPy Array - sample_weight: Numeric NumPy Array

Example Usage¶

>>> import bodo
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=4, n_informative=2,
... random_state=0, shuffle=False)
>>> @bodo.jit
>>> def test_random_forest_regressor(X, y):
...   regr = RandomForestRegressor(max_depth=2)
...   regr.fit(X, y)
...   ans = regr.predict(np.array([[0, 0, 0, 0]]))
...   print(ans)
...
>>> test_random_forest_regressor(X, y)
[-6.7933243]