决策树预测活动类型

import os
import csv
import random
import numpy as np
import pandas as pd
from sklearn.cross_validation import cross_val_score
os.chdir('E:\\HumanActivity')
#加载数据  
data=pd.read_csv('test.csv',sep=',')
print(data.ix[:5])
#决策树预测活动类型
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=14) 
x_previous = data[['timestamp','x','y','z']].values
y_true = data['act_num'].values
scores = cross_val_score(clf,x_previous,y_true,scoring='accuracy')
print("决策树预测准确率: {0:1f}%".format(np.mean(scores) * 100))

预测结果：

决策树预测准确率: 40.238367%

随机森林算法：

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(random_state=14)
x_test = data[['timestamp','x','y','z']].values
scores = cross_val_score(clf,x_test,y_true,scoring='accuracy')
print("随机森林预测准确率: {0:1f}%".format(np.mean(scores) * 100))

预测结果：

随机森林预测准确率: 46.861539%