grid_search_cv.py

from sklearn.datasets import load_diabetes
from sklearn.compose import TransformedTargetRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression

X, y = load_diabetes(return_X_y=True)
ttr = TransformedTargetRegressor()

grid = [{'regressor': [RandomForestRegressor()], 'regressor__n_estimators': [10, 20]},
        {'regressor': [LinearRegression()], 'regressor__fit_intercept': [True, False]}]
reg = GridSearchCV(ttr, grid)
reg.fit(X, y)
reg.best_estimator_