我正在学习ml,并为波士顿房价预测做任务。我有以下代码:
from sklearn.metrics import fbeta_score, make_scorer
from sklearn.model_selection import GridSearchCV
def fit_model(X, y):
""" Tunes a decision tree regressor model using GridSearchCV on the input data X
and target labels y and returns this optimal model. """
# Create a decision tree regressor object
regressor = DecisionTreeRegressor()
# Set up the parameters we wish to tune
parameters = {'max_depth':(1,2,3,4,5,6,7,8,9,10)}
# Make an appropriate scoring function
scoring_function = make_scorer(fbeta_score, beta=2)
# Make the GridSearchCV object
reg = GridSearchCV(regressor, param_grid=parameters, scoring=scoring_function)
print reg
# Fit the learner to the data to obtain the optimal model with tuned parameters
reg.fit(X, y)
# Return the optimal model
return reg.best_estimator_
reg = fit_model(housing_features, housing_prices)
这给了我valueerror:reg.fit(x,y)行不支持continuous,我不明白为什么。这是什么原因,我错过了什么?
最佳答案
那是因为台词:
scoring_function = make_scorer(fbeta_score, beta=2)
这将评分标准设置为fbeta,用于分类任务!
您正在进行回归,如中所示:
regressor = DecisionTreeRegressor()
从the docs