FAFU机器学习03-2 Linear Regression课件.pptxVIP

Foundations of Machine LearningRegression2023/11/4Foundations of Machine Learning RegressionSimple linear regression（简单线性回归）Evaluating the modelMultiple linear regression（多元线性回归）Polynomial regression（多项式回归）Regularization（正则化）Applying linear regressionFitting models with gradient descent（梯度下降）2023/11/4Linear RegressionLesson 3 - 2 Simple linear regressionSimple linear regression can be used to model a linear relationship between one response variable and one explanatory variable. Suppose you wish to know the price of a pizza.2023/11/4Linear RegressionLesson 3 - 3 Observe the data2023/11/4Linear RegressionLesson 3 - 4import matplotlib.pyplot as pltX = [[6], [8], [10], [14], [18]]y = [[7], [9], [13], [17.5], [18]]plt.figure()plt.title(Pizza price plotted against diameter)plt.xlabel(Diameter in inches)plt.ylabel(Price in dollars)plt.plot(X, y, k.)plt.axis([0, 25, 0, 25])plt.grid(True)plt.show() Sklearn.linear_model.LinearRegression2023/11/4Linear RegressionLesson 3 - 5# import sklearnfrom sklearn.linear_model import LinearRegression# Training dataX = [[6], [8], [10], [14], [18]]y = [[7], [9], [13], [17.5], [18]]# Create and fit the modelmodel = LinearRegression()model.fit(X, y)print(A 12 pizza should cost: $%.2f % model.predict([12])[0])# A 12 pizza should cost: $13.68 Sklearn.linear_model.LinearRegressionThe sklearn.linear_model.LinearRegression class is an estimator. Estimators predict a value based on the observed data. In scikit-learn, all estimators implement the fit() and predict() methods. The former method is used to learn the parameters of a model, and the latter method is used to predict the value of a response variable for an explanatory variable using the learned parameters. It is easy to experiment with different models using scikit-learn because all estimators implement the fit and predict methods.2023/11/4Linear RegressionLesson 3 - 6 Results2023/11/4Linear RegressionLesson 3 - 7print((ercept_, model.coef_))Z = model.predict(X)plt.scatter(X, y)plt.plot