问题描述
我有一个 CSV 文件,其中每一行都是一个病人,每一列都是一个“特征”。我想使用这个多元线性回归代码。我想加载我的 CSV 文件,而不是波士顿示例数据集,并使用第 1 到 79 列作为“数据”特征矩阵(X)和第 80 列作为“目标”响应向量(y)。我怎样才能做到这一点?我对 Python 非常陌生,因此非常感谢任何建议。
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets,linear_model,metrics
# load the boston dataset
boston = datasets.load_boston(return_X_y=False)
# defining feature matrix(X) and response vector(y)
X = boston.data
y = boston.target
# splitting X and y into training and testing sets
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4,random_state=1)
# create linear regression object
reg = linear_model.LinearRegression()
# train the model using the training sets
reg.fit(X_train,y_train)
# regression coefficients
print('Coefficients: ',reg.coef_)
# variance score: 1 means perfect prediction
print('Variance score: {}'.format(reg.score(X_test,y_test)))
# plot for residual error
## setting plot style
plt.style.use('fivethirtyeight')
## plotting residual errors in training data
plt.scatter(reg.predict(X_train),reg.predict(X_train) - y_train,color = "green",s = 10,label = 'Train data')
## plotting residual errors in test data
plt.scatter(reg.predict(X_test),reg.predict(X_test) - y_test,color = "blue",label = 'Test data')
## plotting line for zero residual error
plt.hlines(y = 0,xmin = 0,xmax = 50,linewidth = 2)
## plotting legend
plt.legend(loc = 'upper right')
## plot title
plt.title("Residual errors")
## method call for showing the plot
plt.show()
解决方法
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