# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： tf_decision_tree
Description :
Author : Yalye
date： 2018/12/21
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"""
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from pprint import pprint
# step 1: load data
data_path = "./data/"
names = ('animal_name', 'hair', 'feathers', 'eggs', 'milk',
'airbone', 'aquatic', 'predator', 'toothed', 'backbone',
'breathes', 'venomous', 'fins', 'legs', 'tail', 'domestic', 'catsize', 'class',)
formats = ('S1',) + (('i4',) * 17)
zoo_data = np.loadtxt(data_path + 'zoo.data', delimiter=',', dtype={'names': names,
'formats': formats}, skiprows=0)
zoo_data_df = pd.DataFrame(zoo_data)
zoo_data_df = zoo_data_df.drop('animal_name', axis=1)
sample_number = zoo_data.shape[0]
print(sample_number)
print(zoo_data_df)
train_features = zoo_data_df.iloc[:80,:-1]
test_features = zoo_data_df.iloc[80:,:-1]
train_targets = zoo_data_df.iloc[:80,-1]
test_targets = zoo_data_df.iloc[80:,-1]
tree = DecisionTreeClassifier(criterion = 'entropy').fit(train_features,train_targets)
prediction = tree.predict(test_features)
print("The prediction accuracy is: ", tree.score(test_features,test_targets)*100,"%")
结果：
The prediction accuracy is: 80.95238095238095 %
