一、数据截图
二、 代码
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction import DictVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier, export_graphviz
def titanic():
#1.获取数据
path="train.csv"
titanic=pd.read_csv(path)#,nrows=100)
#print(titanic)
#已知数据有下列特征值
#PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
#乘客id 是否生存 阶级 姓名 性别 年龄 有兄弟姐妹 口渴 票 票价 隔间 上船位置
#方法1-缺失值处理-删去
#titanic.dropna(axis=0,how="any",inplace=True)
# 分析选取特征值
x = titanic[["Pclass", "Sex", "Age"]]
# 目标值
y = titanic["Survived"]
#print(x)
#2.数据处理
#0)检查是否存在缺失值(可先将所有数据进行判别
#x_Pclass_exist_nan=x["Pclass"].isnull().values.any()
#x_Sex_exist_nan=x["Sex"].isnull().values.any()
#x_Age_exist_nan=x["Age"].isnull().values.any()
#y_exist_nan=y.isnull().values.any()
# print("特征值Pclass是否有nan：",x_Pclass_exist_nan,
# "\n特征值Sex是否有nan：",x_Sex_exist_nan,
# "\n特征值Age是否有nan：",x_Age_exist_nan)
# print("目标值是否有nan：",y_exist_nan)
#方法2-缺失值处理-平均值
x["Age"].fillna(x["Age"].mean(),inplace=True)
#print(x)
#转换成字典
x=x.to_dict(orient="records")
#3划分数据集
x_train, x_test, y_train, y_test = train_test_split(x,y,random_state=22)
#4字典特征抽取
transfer = DictVectorizer()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
#5决策树预估器
# 加入网格搜索和交叉验证
estimator=RandomForestClassifier()
param_dict = {"n_estimators": [120, 200, 300, 500, 800, 1200],
"max_depth":[5,8,15,25,30]}
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=3)
estimator.fit(x_train, y_train)
#6模型评估
# 1.对比真实值和预测值
y_predict=estimator.predict(x_test)
print("真实值和预测值比对\n",y_test==y_predict)
# 2.计算准确率
score=estimator.score(x_test,y_test)
print("准确率为\n",score)#整体测试集的效果
#网格搜索与交叉验证结果的参数
print("最佳参数\n",estimator.best_params_)
print("最佳结果\n",estimator.best_score_)#验证集中的结果
print("最佳估计器\n",estimator.best_estimator_)
print("交叉验证结果\n",estimator.cv_results_)
#7可视化决策树
export_graphviz(estimator, out_file="./titanic.tree.dot", feature_names=transfer.get_feature_names())
if __name__ == '__main__':
titanic()
三、运行结果
真实值和预测值比对
816 False
789 True
869 False
235 False
473 True
...
174 True
723 True
350 True
399 True
194 True
Name: Survived, Length: 223, dtype: bool
准确率为
0.7713004484304933
最佳参数
{'max_depth': 5, 'n_estimators': 120}
最佳结果
0.8159011028966185
最佳估计器
RandomForestClassifier(max_depth=5, n_estimators=120)
交叉验证结果
{'mean_fit_time': array([0.13299497, 0.21445537, 0.34839487, 0.54510999, 0.88557641,
1.42996311, 0.14926664, 0.24255347, 0.38314478, 0.60953983,
1.00982849, 1.51456213, 0.15878431, 0.26461275, 0.39483476,
0.66195901, 1.05658213, 1.58990987, 0.16172369, 0.26080561,
0.39532431, 0.66376098, 1.06954074, 1.62762157, 0.16098619,
0.26103052, 0.3728656 , 0.65319649, 1.0090692 , 1.5897665 ]), 'std_fit_time': array([0.0020517 , 0.00352949, 0.04416501, 0.02231389, 0.03516453,
0.08532633, 0.00261708, 0.00313477, 0.0252167 , 0.01115087,
0.01610297, 0.02811479, 0.00479312, 0.00383677, 0.00866105,
0.01563949, 0.0052264 , 0.00333935, 0.00379794, 0.00522534,
0.01015787, 0.01360942, 0.02900932, 0.05176689, 0.00399215,
0.01144514, 0.0095812 , 0.0154222 , 0.0168415 , 0.07855189]), 'mean_score_time': array([0.01129794, 0.01760912, 0.0269297 , 0.04188768, 0.06783303,
0.12831823, 0.01164651, 0.01913222, 0.02795378, 0.0453968 ,
0.07681274, 0.11271127, 0.01163594, 0.0202926 , 0.02926898,
0.04853662, 0.078336 , 0.11386808, 0.01196702, 0.02012944,
0.02891843, 0.04953392, 0.07670363, 0.12533696, 0.01163022,
0.01925429, 0.02759449, 0.04856451, 0.07310772, 0.1094408 ]), 'std_score_time': array([1.21565231e-03, 4.64426949e-04, 1.40815497e-03, 8.77806426e-07,
8.14279704e-04, 2.85228987e-02, 4.79448361e-04, 2.90959658e-04,
7.97307765e-04, 6.99545003e-04, 8.38448756e-04, 1.41999678e-03,
9.41111034e-04, 1.68392675e-03, 1.67678405e-03, 1.88199732e-03,
1.20660599e-03, 2.76808664e-03, 8.13614765e-04, 2.29377756e-04,
8.14112007e-04, 3.08379231e-03, 1.06598089e-03, 9.68575782e-03,
4.79575098e-04, 4.60008011e-04, 1.24437035e-03, 1.69949754e-03,
2.60600291e-03, 2.12582777e-03]), 'param_max_depth': masked_array(data=[5, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8, 8, 15, 15, 15, 15, 15,
15, 25, 25, 25, 25, 25, 25, 30, 30, 30, 30, 30, 30],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object), 'param_n_estimators': masked_array(data=[120, 200, 300, 500, 800, 1200, 120, 200, 300, 500, 800,
1200, 120, 200, 300, 500, 800, 1200, 120, 200, 300,
500, 800, 1200, 120, 200, 300, 500, 800, 1200],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False],
fill_value='?',
dtype=object), 'params': [{'max_depth': 5, 'n_estimators': 120}, {'max_depth': 5, 'n_estimators': 200}, {'max_depth': 5, 'n_estimators': 300}, {'max_depth': 5, 'n_estimators': 500}, {'max_depth': 5, 'n_estimators': 800}, {'max_depth': 5, 'n_estimators': 1200}, {'max_depth': 8, 'n_estimators': 120}, {'max_depth': 8, 'n_estimators': 200}, {'max_depth': 8, 'n_estimators': 300}, {'max_depth': 8, 'n_estimators': 500}, {'max_depth': 8, 'n_estimators': 800}, {'max_depth': 8, 'n_estimators': 1200}, {'max_depth': 15, 'n_estimators': 120}, {'max_depth': 15, 'n_estimators': 200}, {'max_depth': 15, 'n_estimators': 300}, {'max_depth': 15, 'n_estimators': 500}, {'max_depth': 15, 'n_estimators': 800}, {'max_depth': 15, 'n_estimators': 1200}, {'max_depth': 25, 'n_estimators': 120}, {'max_depth': 25, 'n_estimators': 200}, {'max_depth': 25, 'n_estimators': 300}, {'max_depth': 25, 'n_estimators': 500}, {'max_depth': 25, 'n_estimators': 800}, {'max_depth': 25, 'n_estimators': 1200}, {'max_depth': 30, 'n_estimators': 120}, {'max_depth': 30, 'n_estimators': 200}, {'max_depth': 30, 'n_estimators': 300}, {'max_depth': 30, 'n_estimators': 500}, {'max_depth': 30, 'n_estimators': 800}, {'max_depth': 30, 'n_estimators': 1200}], 'split0_test_score': array([0.79820628, 0.79820628, 0.79820628, 0.79820628, 0.79820628,
0.79820628, 0.75784753, 0.77130045, 0.75784753, 0.76233184,
0.75784753, 0.75784753, 0.78026906, 0.78026906, 0.78026906,
0.78475336, 0.78475336, 0.78475336, 0.79372197, 0.78026906,
0.78475336, 0.78475336, 0.78026906, 0.78026906, 0.78475336,
0.78026906, 0.78026906, 0.78475336, 0.78026906, 0.78475336]), 'split1_test_score': array([0.81165919, 0.81165919, 0.81165919, 0.81165919, 0.81165919,
0.81165919, 0.79820628, 0.80269058, 0.80269058, 0.80269058,
0.79372197, 0.79372197, 0.79820628, 0.78026906, 0.78475336,
0.78026906, 0.78026906, 0.78026906, 0.78026906, 0.78026906,
0.78026906, 0.78026906, 0.78026906, 0.78026906, 0.78026906,
0.78026906, 0.78026906, 0.78026906, 0.78026906, 0.78026906]), 'split2_test_score': array([0.83783784, 0.83783784, 0.83333333, 0.83783784, 0.83783784,
0.81081081, 0.85135135, 0.82432432, 0.83783784, 0.83333333,
0.83783784, 0.84684685, 0.81081081, 0.82432432, 0.82882883,
0.82882883, 0.84684685, 0.82882883, 0.85135135, 0.84684685,
0.82882883, 0.82882883, 0.84234234, 0.84234234, 0.82432432,
0.81531532, 0.82432432, 0.82882883, 0.84684685, 0.82882883]), 'mean_test_score': array([0.8159011 , 0.8159011 , 0.8143996 , 0.8159011 , 0.8159011 ,
0.80689209, 0.80246839, 0.79943845, 0.79945865, 0.79945192,
0.79646911, 0.79947212, 0.79642872, 0.79495415, 0.79795042,
0.79795042, 0.80395642, 0.79795042, 0.80844746, 0.80246165,
0.79795042, 0.79795042, 0.80096015, 0.80096015, 0.79644892,
0.79195114, 0.79495415, 0.79795042, 0.80246165, 0.79795042]), 'std_test_score': array([0.0164552 , 0.0164552 , 0.01447089, 0.0164552 , 0.0164552 ,
0.00615156, 0.03829156, 0.02176871, 0.03273577, 0.02907656,
0.03271363, 0.03656061, 0.01253181, 0.02076785, 0.02191095,
0.02191095, 0.03038331, 0.02191095, 0.03083075, 0.03138507,
0.02191095, 0.02191095, 0.02926163, 0.02926163, 0.01979572,
0.01652096, 0.02076785, 0.02191095, 0.03138507, 0.02191095]), 'rank_test_score': array([ 1, 1, 5, 1, 1, 7, 9, 17, 15, 16, 25, 14, 27, 28, 18, 18, 8,
18, 6, 10, 18, 18, 12, 12, 26, 30, 28, 18, 10, 18])}
