inspection.importance.generate_imp
inspection.importance.generate_imp(
X_train,
X_test,
y_train,
y_test,
random_state=42,
n_repeats=10,
use_test=True,
)
Generates permutation importances from a RandomForestRegressor.
Parameters
| X_train |
pd.DataFrame or np.ndarray |
The training feature set. |
required |
| X_test |
pd.DataFrame or np.ndarray |
The test feature set. |
required |
| y_train |
pd.Series or np.ndarray |
The training target variable. |
required |
| y_test |
pd.Series or np.ndarray |
The test target variable. |
required |
| random_state |
int |
Random state for the RandomForestRegressor. Defaults to 42. |
42 |
| n_repeats |
int |
Number of repeats for permutation importance. Defaults to 10. |
10 |
| use_test |
bool |
If True, computes permutation importance on the test set. If False, uses the training set. Defaults to True. |
True |
Returns
| permutation_importance |
permutation_importance |
Permutation importances object. |
Examples
>>> from spotoptim.sensitivity.importance import generate_imp
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=42)
>>> X_train, X_test = X[:80], X[80:]
>>> y_train, y_test = y[:80], y[80:]
>>> X_train_df = pd.DataFrame(X_train)
>>> X_test_df = pd.DataFrame(X_test)
>>> y_train_series = pd.Series(y_train)
>>> y_test_series = pd.Series(y_test)
>>> perm_imp = generate_imp(X_train_df, X_test_df, y_train_series, y_test_series)
>>> print(perm_imp)