Python 决策树 泰坦尼克号乘客是否生还决策模型

与网上的其他内容均一样

import pandas as pd
titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')

titanic.head()

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	row.names	pclass	survived	name	age	embarked	home.dest	room	ticket	boat	sex
0	1	1st	1	Allen, Miss Elisabeth Walton	29.0000	Southampton	St Louis, MO	B-5	24160 L221	2	female
1	2	1st	0	Allison, Miss Helen Loraine	2.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	NaN	female
2	3	1st	0	Allison, Mr Hudson Joshua Creighton	30.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	(135)	male
3	4	1st	0	Allison, Mrs Hudson J.C. (Bessie Waldo Daniels)	25.0000	Southampton	Montreal, PQ / Chesterville, ON	C26	NaN	NaN	female
4	5	1st	1	Allison, Master Hudson Trevor	0.9167	Southampton	Montreal, PQ / Chesterville, ON	C22	NaN	11	male

titanic.info()

X = titanic[['pclass', 'age', 'sex']]
y = titanic['survived']

X.info()
X.head()

# 使用均值对AGE进行插值
X['age'].fillna(X['age'].mean(), inplace=True)
X.info()

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


D:\Program Files\Anaconda35\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.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=33)
X_train.head()
X_train.info()

Int64Index: 984 entries, 1086 to 1044
Data columns (total 3 columns):
pclass    984 non-null object
age       984 non-null float64
sex       984 non-null object
dtypes: float64(1), object(2)
memory usage: 30.8+ KB

from sklearn.feature_extraction import DictVectorizer

# 原文 vec = DictVectorizer(sparse=False) 报错
vec = DictVectorizer()

X_train = vec.fit_transform(X_train.to_dict(orient = 'record'))  
X_test = vec.transform(X_test.to_dict(orient = 'record'))
#X_train.to_dict(orient='record')
vec.feature_names_

['age', 'pclass=1st', 'pclass=2nd', 'pclass=3rd', 'sex=female', 'sex=male']

from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
y_predict = dtc.predict(X_test)

from sklearn.metrics import classification_report
print(dtc.score(X_test, y_test))

0.781155015198

print(classification_report(y_predict, y_test, target_names=['died', 'survived']))

             precision    recall  f1-score   support

       died       0.91      0.78      0.84       236
   survived       0.58      0.80      0.67        93

avg / total       0.81      0.78      0.79       329