刪除離群後的房價

依前一篇的 LOF 將離群值刪除後，再重新進行訓練及預測，結果可以將分數拉到 0.81。如果把 LSTAT ** (1/3)，則可以拉高到 0.85

底下為完整代碼

from sklearn.datasets import load_boston
import pandas as pd
import seaborn as sns
import pylab as plt
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor as LOF
display=pd.options.display
display.max_columns=None
display.max_rows=None
display.width=None
display.max_colwidth=None

boston_dataset=load_boston()
df=pd.DataFrame(data=boston_dataset.data, columns=boston_dataset.feature_names)
df.insert(0, column="PRICE", value=boston_dataset.target)
df=df[['PRICE','LSTAT','RM']]

x=df[['LSTAT','RM']]
y=df['PRICE']

lof=LOF(n_neighbors=20, contamination='auto')
y_pred=lof.fit_predict(np.c_[x, y])
df=pd.DataFrame(data=df.loc[np.where(y_pred==1)].values, columns=['PRICE','LSTAT','RM'])
print(df.shape)


data=np.c_[df['LSTAT']**(1/3),df['RM']]
x=pd.DataFrame(data=data, columns=['LSTAT','RM'])
# x=df[['LSTAT','RM']]
y=df['PRICE']

#資料切割 80%訓練, 20%測試
x_train, x_test, y_train, y_test=train_test_split(x, y, test_size=0.2, random_state=5)
model=LinearRegression()#線性迴歸模型
model.fit(x_train, y_train)#模型訓練，資料量大時，會花很久的時間
#分數愈接近1, 表示愈準確
print(f'分數 : {model.score(x_test, y_test)}')

#開始預測價格
y_pred=model.predict(x_test)
for i in zip(y_pred, y_test):
    print(i)
結果 : 
(455, 3)
分數 : 0.8500263734728444
(17.684860535122077, 17.4)
(27.556142791275335, 25.3)
(30.90162563908891, 36.1)
(29.183795216204057, 32.2)
(25.254389371333477, 22.0)