Question

我是python＆ML的新手，但是我试图使用sklearn来构建决策树。我具有许多分类特征，并将其转换为数值变量。但是，我的目标功能是多类，并且遇到了错误。我应该如何处理多类目标？

ValueError：目标是多类的，但average ='binary'。请选择另一个平均设置，[无，'微'，'宏'，'加权']。

from sklearn.model_selection import train_test_split

#SPLIT DATA INTO TRAIN AND TEST SET
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size =0.30, #by default is 75%-25%
                                                    #shuffle is set True by default,
                                                    stratify=y, #preserve target propotions 
                                                    random_state= 123) #fix random seed for replicability

print(X_train.shape, X_test.shape)


from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(criterion='gini', max_depth=3, min_samples_split=4, min_samples_leaf=2)

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# criterion : "gini", "entropy"
# max_depth : The maximum depth of the tree.
# min_samples_split : The minimum number of samples required to split an internal node:
# min_samples_leaf : The minimum number of samples required to be at a leaf node. 

#DEFINE YOUR CLASSIFIER and THE PARAMETERS GRID
from sklearn.tree import DecisionTreeClassifier
import numpy as np

classifier = DecisionTreeClassifier()
parameters = {'criterion': ['entropy','gini'], 
              'max_depth': [3,4,5],
              'min_samples_split': [5,10],
              'min_samples_leaf': [2]}

from sklearn.model_selection import GridSearchCV
gs = GridSearchCV(classifier, parameters, cv=3, scoring = 'f1', verbose=50, n_jobs=-1, refit=True)

enter image description here

Answer 1

您应该手动指定分数功能：

from sklearn.metrics import f1_score, make_scorer

f1 = make_scorer(f1_score, average='weighted')

....

gs = GridSearchCV(classifier, parameters, cv=3, scoring=f1, verbose=50, n_jobs=-1, refit=True)

Answer 2

非常感谢您的帮助。我想到了。它实际上在gs线上。在计分中，我需要调整您提到的内容。所以我修改了分数= f1_macro

gs = GridSearchCV(classifier, parameters, cv=3, scoring=f1_macro, verbose=50, n_jobs=-1, refit=True)

我如何处理多类决策树？

2 个答案: