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如何提高线性回归模型的准确性?(使用python进行机器学习)

我有一个使用scikit-learn库的python机器学习项目.我有两个用于训练和测试的分离数据集,我尝试进行线性回归.我使用下面显示的代码块:

import numpy as np
import pandas as pd
import scipy
import matplotlib.pyplot as plt
from pylab import rcParams
import urllib
import sklearn
from sklearn.linear_model import LinearRegression
df =pd.read_csv("TrainingData.csv")
df2=pd.read_csv("TestingData.csv")

df['Development_platform']= ["".join("%03d" % ord(c) for c in s) for s in df['Development_platform']]
df['Language_Type']= ["".join("%03d" % ord(c) for c in s) for s in df['Language_Type']]


df2['Development_platform']= ["".join("%03d" % ord(c) for c in s) for s in df2['Development_platform']]
df2['Language_Type']= ["".join("%03d" % ord(c) for c in s) for s in df2['Language_Type']]

X_train = …
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python machine-learning scikit-learn

3
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2
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machine-learning ×1

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