关于决策树的理论知识可以查看：决策树
Titanic数据集概述
RMS泰坦尼克号的沉没是历史上最臭名昭着的沉船之一。 194月15日，在首航期间，泰坦尼克号撞上一座冰山后沉没，2224名乘客和机组人员中有1502人遇难。这一耸人听闻的悲剧震撼了国际社会，导致了更好的船舶安全条例。预测是否有乘客幸存下来的泰坦尼克号。
代码实战
首先，一如既往导入数据集，并查看一下部分数据
import pandas as pdtitanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')titanic.head()
info可以查看一下数据集的基本信息，我们可以看到数据集中有部分缺失值
titanic.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> survived1313 non-null int64> name 1313 non-null object> age633 non-null float64> embarked821 non-null object> home.dest 754 non-null object> room 77 non-null object> ticket 69 non-null object> boat 347 non-null object> sex1313 non-null object> dtypes: float64(1), int64(2), object(8)> memory usage: 112.9+ KB
我们从中选取三个特征来对数据进行预测
X = titanic[['pclass','age','sex']]y = titanic['survived']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
age具有缺失值，所以我们要对缺失值进行处理
X['age'].fillna(X['age'].mean(),inplace=True)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
之后就是将数据集划分成训练集和测试集
from sklearn.cross_validation import train_test_splitX_train,X_test,Y_train,Y_test = train_test_split(X,y,test_size=0.25,random_state=33)from sklearn.feature_extraction import DictVectorizervec = DictVectorizer(sparse=False)X_train = vec.fit_transform(X_train.to_dict(orient='record'))print(vec.feature_names_)X_test = vec.fit_transform(X_test.to_dict(orient='record'))
[‘age’, ‘pclass=1st’, ‘pclass=2nd’, ‘pclass=3rd’, ‘sex=female’, ‘sex=male’]
导入决策树模型，并进行预测
from sklearn.tree import DecisionTreeClassifierdtc = DecisionTreeClassifier()dtc.fit(X_train,Y_train)y_predict = dtc.predict(X_test)
最后，检查一下模型的效果
from sklearn.metrics import classification_reportprint(dtc.score(X_test,Y_test))print(classification_report(y_predict,Y_test,target_names=['died','survived']))
代码参考：《Python机器学习及实践：从零开始通往Kaggle竞赛之路》
