基于sklearn的决策树分类器
2017-11-12 本文已影响0人
月见樽
理论基础
决策树
决策树是一种树形结构的机器学习算法，所有的样本起始于根节点，每个具有子节点的父节点都有一个判断，根据判断结果将样本向子节点分流，测试样本从根节点开始向下流动，通过判断最终到达某个没有子节点的叶子节点，这个节点就是该样本所属的类别。
例如，判断一个动物是鸭子，狗还是兔子，可以具有以下的决策树：
- 判断是否有四条腿
- 没有，是鸭子
- 有，判断眼睛颜色
- 红色，是兔子
- 非红色，是狗
决策树训练算法
训练决策树时，可以描述如下
- 从父节点找到最优划分属性
- 根据属性划分出子节点
- 若子节点为空/属性相同（无需划分）或样本相等（无法划分），返回，否则返回第一步继续递归划分
找到最优划分属性时，计算按每个属性划分的信息熵，取信息熵最大的属性为最优划分属性
代码实现
载入数据——泰坦尼克号数据导入
import pandas as pd
titan = pd.read_csv("http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt")
print(titan.head())
row.names pclass survived \
0 1 1st 1
1 2 1st 0
2 3 1st 0
3 4 1st 0
4 5 1st 1
name age embarked \
0 Allen, Miss Elisabeth Walton 29.0000 Southampton
1 Allison, Miss Helen Loraine 2.0000 Southampton
2 Allison, Mr Hudson Joshua Creighton 30.0000 Southampton
3 Allison, Mrs Hudson J.C. (Bessie Waldo Daniels) 25.0000 Southampton
4 Allison, Master Hudson Trevor 0.9167 Southampton
home.dest room ticket boat sex
0 St Louis, MO B-5 24160 L221 2 female
1 Montreal, PQ / Chesterville, ON C26 NaN NaN female
2 Montreal, PQ / Chesterville, ON C26 NaN (135) male
3 Montreal, PQ / Chesterville, ON C26 NaN NaN female
4 Montreal, PQ / Chesterville, ON C22 NaN 11 male
print(titan.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 11 columns):
row.names 1313 non-null int64
pclass 1313 non-null object
survived 1313 non-null int64
name 1313 non-null object
age 633 non-null float64
embarked 821 non-null object
home.dest 754 non-null object
room 77 non-null object
ticket 69 non-null object
boat 347 non-null object
sex 1313 non-null object
dtypes: float64(1), int64(2), object(8)
memory usage: 112.9+ KB
None
数据预处理
选取特征
x = titan[["pclass","age","sex"]]
y = titan["survived"]
print(x.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 3 columns):
pclass 1313 non-null object
age 633 non-null float64
sex 1313 non-null object
dtypes: float64(1), object(2)
memory usage: 30.9+ KB
None
年龄补全——使用平均值
x['age'].fillna(x['age'].mean(),inplace=True)
print(x.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1313 entries, 0 to 1312
Data columns (total 3 columns):
pclass 1313 non-null object
age 1313 non-null float64
sex 1313 non-null object
dtypes: float64(1), object(2)
memory usage: 30.9+ KB
None
c:\users\qiank\appdata\local\programs\python\python35\lib\site-packages\pandas\core\generic.py:3660: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
self._update_inplace(new_data)
数据分割
from sklearn.cross_validation import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=1)
print(x_train.shape)
print(x_test.shape)
(984, 3)
(329, 3)
特征转换
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer(sparse=False)
x_train = vec.fit_transform(x_train.to_dict(orient='record'))
x_test = vec.transform(x_test.to_dict(orient='record'))
print(vec.feature_names_,"\n",x_train[:5])
['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', 'sex=female', 'sex=male']
[[ 31.19418104 0. 0. 1. 0. 1. ]
[ 31.19418104 0. 0. 1. 0. 1. ]
[ 35. 0. 1. 0. 1. 0. ]
[ 31. 0. 1. 0. 0. 1. ]
[ 26. 0. 0. 1. 0. 1. ]]
调用决策树分类器
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(x_train,y_train)
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
模型评估
自带评估
dtc.score(x_test,y_test)
0.81155015197568392
评估器
from sklearn.metrics import classification_report
y_pre = dtc.predict(x_test)
print(classification_report(y_pre,y_test,target_names=["died","survived"]))
precision recall f1-score support
died 0.91 0.80 0.85 226
survived 0.66 0.83 0.74 103
avg / total 0.83 0.81 0.82 329
