樣本準備

import numpy as np
import matplotlib.pyplot as plt

# 樣本準備
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3  # y=2x，但是加入了噪聲
# 顯示模擬資料點
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

模型訓練及視覺化

from sklearn.linear_model import
 
 LinearRegression

model = LinearRegression()
model.fit(train_X.reshape(100, 1), train_Y.reshape(100, 1))
print("輸入6，的模型預測結果：", model.predict(6))
print("線性模型的斜率與截距：", model.coef_, model.intercept_)
# y = kx+b
print("使用斜率與截距的計算結果：", model.coef_ * 6 + model.intercept_)

plt.plot(train_X, train_Y, 'ro', label=
 
'Original data')
plt.plot(train_X, model.predict(train_X.reshape(100, 1)), label='Fitted line')
plt.legend()
plt.show()

模型評估

X_test = np.linspace(11, 20, 20)
Y_test = 2 * X_test + np.random.randn(*X_test.shape) * 0.3
print("模型評估的分值：", model.score(X_test.reshape(20, 1), Y_test.reshape(20, 1)))

模型儲存，及應用

from
 
 sklearn.externals import joblib

joblib.dump(model, "train_model.m")
model = joblib.load("train_model.m")
print("匯入模型，並輸入6得到的預測結果：", model.predict(6))