决策树分类器的准确性得分

问题描述

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)

我无法使用以上代码来计算DecisionTreeClassifier的准确性.有人可以帮我吗?

I can't calculate the accuracy of a DecisionTreeClassifier using the above code.Can somebody help me with this?

推荐答案

问题是您正在混淆事物.对比训练标签和测试标签并不意味着计算准确性.

The problem is that you are mixing up things. It doesn't mean anything to compute the accuracy comparing the train and test labels.

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)

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