python – GridSearchCV给出ValueError：DecisionTreeRegressor不支持continuous

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)行不支持连续,我不明白为什么.这是什么原因,我在这里错过了什么？

解决方法:

那是因为这条线：

scoring_function = make_scorer(fbeta_score, beta=2)

这将得分指标设置为fbeta,这是分类任务！

你在这里做回归,如下所示：

regressor = DecisionTreeRegressor()

从the docs开始

标签：decision-tree,grid-search,python,scikit-learn,machine-learning
来源： https://codeday.me/bug/20190828/1749364.html