为什么我所有的后悔器显示的准确度都比我所有的分类器低得多

Question

我正在测试下面的一些示例代码。所有分类结果都非常合理(80%或以上(。所有回归结果都非常糟糕，而且非常异常(约20%(。为什么会这样？我一定做错了什么，但我看不出这里发生了什么。

import pandas as pd
import numpy as np
#reading the dataset
df=pd.read_csv("C:\my_path\train.csv")
#filling missing values
df['Gender'].fillna('Male', inplace=True)
df.fillna(0)
df.Loan_Status.replace(('Y', 'N'), (1, 0), inplace=True)
#split dataset into train and test
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.3, random_state=0)
x_train=train.drop(['Loan_Status','Loan_ID'],axis=1)
y_train=train['Loan_Status']
x_test=test.drop(['Loan_Status','Loan_ID'],axis=1)
y_test=test['Loan_Status']
#create dummies
x_train=pd.get_dummies(x_train)
x_test=pd.get_dummies(x_test)

# Baggin Classifier
from sklearn.ensemble import BaggingClassifier
from sklearn import tree
model = BaggingClassifier(tree.DecisionTreeClassifier(random_state=1))
model.fit(x_train, y_train)
model.score(x_test,y_test)

# Bagging Regressor
from sklearn.ensemble import BaggingRegressor
model = BaggingRegressor(tree.DecisionTreeRegressor(random_state=1))
model.fit(x_train, y_train)
model.score(x_test,y_test)

# AdaBoostClassifier
from sklearn.ensemble import AdaBoostClassifier
model = AdaBoostClassifier(random_state=1)
model.fit(x_train, y_train)
model.score(x_test,y_test)

# AdaBoostRegressor
from sklearn.ensemble import AdaBoostRegressor
model = AdaBoostRegressor()
model.fit(x_train, y_train)
model.score(x_test,y_test)

# GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingClassifier
model= GradientBoostingClassifier(learning_rate=0.01,random_state=1)
model.fit(x_train, y_train)
model.score(x_test,y_test)
# GradientBoostingRegressor
from sklearn.ensemble import GradientBoostingRegressor
model= GradientBoostingRegressor()
model.fit(x_train, y_train)
model.score(x_test,y_test)

# XGBClassifier
import xgboost as xgb
model=xgb.XGBClassifier(random_state=1,learning_rate=0.01)
model.fit(x_train, y_train)
model.score(x_test,y_test)

# XGBRegressor
import xgboost as xgb
model=xgb.XGBRegressor()
model.fit(x_train, y_train)
model.score(x_test,y_test)

示例数据来自下面的链接。

https://www.kaggle.com/wendykan/lending-club-loan-data

最后，这里是我所看到的一个小样本。

# Bagging Regressor
from sklearn.ensemble import BaggingRegressor
regressor = BaggingRegressor()
regressor.fit(x_train,y_train)
accuracy = regressor.score(x_test,y_test)
print(accuracy*100,'%')
# result:
13.022388059701505 %
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x_train,y_train)
accuracy = regressor.score(x_test,y_test)
print(accuracy*100,'%')
# result:
29.836209522493196 %

Answer 1

回归和分类是两项不同的任务。从您的代码来看，您似乎正在尝试使用与分类器相同的数据来拟合回归器。基本上，重新求解器试图找到一个根据输入来猜测输出数的函数。因此，目标值应该是来自连续空间的数字，而不是类别。例如，你可能想根据贷款金额来预测贷款人的收入。

有关回归和分类之间差异的更多信息，请查看此媒体页面。