Python 属性错误：'；DecisionTreeRegressor'；对象没有属性'；损失'；

我正在调整

DecisionTreeRegressor

的超参数，如下所示

from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import numpy as np
import tensorflow as tf
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import f1_score, make_scorer

housing = fetch_california_housing()

seed1 = 7
seed2 = 5
seed3 = 42

X_train_full, X_test, y_train_full, y_test = train_test_split(housing.data, housing.target, random_state=seed1)
X_train, X_valid, y_train, y_valid = train_test_split(X_train_full, y_train_full, random_state=seed2)

def build_model(max_depth=3, min_samples_leaf=3, max_leaf_nodes=4):
    clf = DecisionTreeRegressor(max_depth,
                                min_samples_leaf,
                                max_leaf_nodes, random_state=seed3)
    return clf

keras_clf = tf.keras.wrappers.scikit_learn.KerasRegressor(build_model)

param_spec = {
    "max_depth": np.arange(2,10).tolist(),
    "min_samples_leaf": np.arange(1,100).tolist(),
    "max_leaf_nodes": np.arange(1,5).tolist()
}

f1_scorer = make_scorer(f1_score)

rnd_search_cv = GridSearchCV(keras_clf, param_spec, scoring=f1_scorer, cv=3, n_jobs=-1, verbose=1)
rnd_search_cv.fit(X_train, y_train, epochs=50,
                  validation_data=(X_valid, y_valid))

print(rnd_search_cv.best_params_)
print(rnd_search_cv.best_score_)

但会引发以下错误：

if (losses.is_categorical_crossentropy(self.model.loss) and
AttributeError: 'DecisionTreeRegressor' object has no attribute 'loss'

---

我没有在我的模型中指定任何损失，而且，好吧，我不必在这个场景中设置一个，在我的

KerasRegressor

中设置

评分。那么，如何解决这个问题呢？
您似乎使用Keras的scikit learn包装器来包装
决策树。这是没有必要的（实际上是错误的）。。。只需将
build\u model
返回的决策树直接传递到
GridSearchCV
。