决策树分类器的准确度分数

Question

import sys
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

import numpy as np
import pylab as pl

features_train, labels_train, features_test, labels_test = makeTerrainData()
X = features_train
Y = labels_train
clf = DecisionTreeClassifier()
clf = clf.fit(X,Y)
labels_test = clf.predict(features_test)

acc = accuracy_score(labels_test, labels_train)

我无法使用上述代码计算DecisionTreeClassifier的准确性。 有人可以帮我吗？

Answer 1

问题在于你是在混淆东西。与列车和测试标签相比，计算精度并不意味着什么。

请执行以下操作：

features_train, labels_train, features_test, labels_test = makeTerrainData()
X = features_train
Y = labels_train
clf = DecisionTreeClassifier()
clf = clf.fit(X,Y)
# Here call it somehing else!
yhat_test = clf.predict(features_test)
# Compute accuracy based on test samples
acc = accuracy_score(labels_test, yhat_test)

Answer 2

进行此更改

predicted = clf.predict（features_test）

acc =准确度得分（labels_test，已预测）