Scikit-Learn

使用Scikit-Learn进行线性回归

Scikit-learn 有一个梯度下降回归模型sklearn.linear_model.SGDRegressor。sklearn.preprocessing.StandardScaler会像之前的实验一样，对 z 值进行归一化处理。在这里，它被称为 ‘standard score’

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDRegressor
from sklearn.preprocessing import StandardScaler
from lab_utils_multi import  load_house_data
from lab_utils_common import dlc
np.set_printoptions(precision=2)

加载数据集

X_train, y_train = load_house_data()
X_features = ['size(sqft)','bedrooms','floors','age']

对训练数据进行缩放/归一化处理

sgdr = SGDRegressor(max_iter=1000)
sgdr.fit(X_norm, y_train)
print(sgdr)
print(f"number of iterations completed: {sgdr.n_iter_}, number of weight updates: {sgdr.t_}")

查看参数

b_norm = sgdr.intercept_
w_norm = sgdr.coef_
print(f"model parameters:                   w: {w_norm}, b:{b_norm}")
print( "model parameters from previous lab: w: [110.56 -21.27 -32.71 -37.97], b: 363.16")

进行预测

预测训练数据的目标。同时使用预测例程和计算方法 $𝑤$ 和 $𝑏$

# make a prediction using sgdr.predict()
y_pred_sgd = sgdr.predict(X_norm)
# make a prediction using w,b. 
y_pred = np.dot(X_norm, w_norm) + b_norm  
print(f"prediction using np.dot() and sgdr.predict match: {(y_pred == y_pred_sgd).all()}")

print(f"Prediction on training set:\n{y_pred[:4]}" )
print(f"Target values \n{y_train[:4]}")