• 提高代码规范性，基于上一个 baseline 的提高

import pandas as pd
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split#数据划分方法
from sklearn.ensemble import RandomForestClassifier#随机森林
from sklearn.model_selection import GridSearchCV #网格交叉搜索交叉验证

# data = pd.read_csv("file_path")打印出来看

def data_clean(file_path):
	""""
	数据处理函数
	parameters:
	file_path:数据文件路径
	""""
	data = pd.read_csv(file_path)
	#删除不需要的列
	data.drop("PassengerId",axis=1,inplace=True)
	data.drop(["Name","Ticket","Cabin"],axis=1,inplace=True)
	#对空值进行填充
	data["Age"].fillna(data["Age"].mean(),inplace=True)
	#对字符的东西进行一个空值填充
	data["Embarked"].fillna(data["Embarked"].mode[0],inplace=True)
	#数据preprocessing
	data["Sex"]=LabelBinarizer().fit_transform(data["Sex"])
	data = pd.get_dummied(data)
	#缩放，减少计算量
	data["Fare"] = StandardScaler().fit_transform(data["Fare"].values.reshape(-1,1))

	return (data)

# 数据划分
def data_split(data):
	""""
	数据划分函数
	parameters:
	data:要划分的数据
	""""
	x = data.drop(["Survived"],axis=1)
	y = data["Survived"]
	x_train,x_test,y_train,y_test = train_test.split(x,y,test_size=0.2)
	return (x_train,x_test,y_train,y_test)

# 模型搭建
def model_fit(x,y):
	"""
	模型训练函数
	parameters:
	x:特征
	y:标签
	"""
	#定义好参数
	Para_grid = [{"n_estimators":[3,10,30],"max_features":[2,4,6,8]},
	{"bootstrap",[False],"n_estimators":[3,10],"max_features":[2,4,6]}]
	#模型实例化
	model = RandomForestClassifier()
	#模型和交叉验证次数
	gird_search = GridSearchCV(model,Para_grid,cv=5)
	#模型训练
	grid_search.fit(x,y)
	
	return(grid_search.best_params_,grid_search.best_estimator_)

data = data_clean("data/train.csv")
x_train,x_test,y_train,y_test = data_split(data)
model_fit(x_train,y_train)
# 上一行得到最优的模型和参数
model = RandomForestClassifier(n_estimators=30,max_features=2,max_depth=100)
model.fit(x_train,y_train)
model.score(x_test,y_test)

#程序入口
if __name__ == '__main__':
	A
	B
	C

• 模型融合，去 sklearn 看一下就懂