33、机器学习分类器与自然语言处理应用

机器学习分类器与自然语言处理应用

1. 机器学习中的线性回归

1.1 Sklearn 实现线性回归

Sklearn 可用于在欧几里得平面上对一组随机生成的点进行线性回归，并绘制出这些点和最拟合的直线。以下是实现代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

x = 10*np.random.rand(100)
y = 5*x + 5*np.random.rand(100)

print("=> first 10 x values:")
print(x[0:10])
print("=> max x value:",x.max())
print()
print("=> first 10 y values:")
print(y[0:10])
print("=> max y value:",y.max())
print()

model = LinearRegression(fit_intercept=True)
X = x.reshape(-1, 1)
print("X.shape:",X.shape)
model.fit(X, y)
print("=> slope of line:", model.coef_[0])
print("=> y-intercept:  ", model.intercept_)

x_fit = np.linspace(-1,11)
X_fit = x_f