(一)单变量线性回归 Linear Regression with One Variable (二)多变量线性回归 Linear Regression with Multiple Variables (三)逻辑回归 Logistic Regression (四)正则化与过拟合问题 Regularization/The Problem of Overfitting (五)神经网络的表示 Neural Networks:Representation (六)神经网络的学习 Neural Networks:Lear...
1. Multiple features(多维特征) 在机器学习之单变量线性回归(Linear Regression with One Variable)我们提到过的线性回归中,我们只有一个单一特征量(变量)——房屋面积x。我们希望使用这个特征量来预测房子的价格。我们的假设在下图中用蓝线划出: 不妨思考一下,如果我们不仅仅知道房屋面积(作为预测房屋价格的特征量(...
import math; def sum_of_gradient(x, y, thetas): """计算梯度向量,参数分别是x和y轴点坐标数据以及方程参数""" m = len(x); grad0 = 1.0 / m * sum([(thetas[0] + thetas[1] * x[i] - y[i]) for i in range(m)]) grad1 = 1.0 / m * sum([(thetas[0] + thetas[1] *...
吴恩达《Machine Learning》-Linear Regression with Multiple Variables多元线性回归(四),程序员大本营,技术文章内容聚合第一站。
4 多变量线性回归(Linear Regression with Multiple Variables) 4.1 多特征(Multiple Features) 对于一个要度量的对象,一般来说会有不同维度的多个特征。比如之前的房屋价格预测例子中,除了房屋的面积大小,可能还有房屋的年限、房屋的层数等等其他特征: 这里由于特征不再只有一个,引入一些新的记号 ...
【Machine Learning】4 多变量线性回归(Linear Regression with Multiple Variables),程序员大本营,技术文章内容聚合第一站。
[Section 1] Multiple Features [Section 2] Gradient Descent for Multiple Variables [Section 3] Gradient Descent in Practice I - Feature Scaling [Section 4] Gradient Descent in Practice II - Learning Rate [Section 5] Features and Polynomial Regression ...
Linear Algebra Multiple Variable Linear Regression Logistic Regression Neural Networks (Representation) Neural Networks (Learning) Applying Machine Learning Machine Learning Systems Design Support Vector Machines Unsupervised Learning Theory Unsupervised Learning Dimensionality Reduction Anomaly Detection Recommender Sy...
regression modelingThis chapter discusses methods for predictor variable selection by using two examples. The first example deals with the Hald cement data which is a small data set that involves extreme multicollinearity, and the second example deals with the used car prices data which is a large...
A Multiple Linear Regression (MLR) model for a response variable Y and ex- planatory variables X 1 , X 2 , . . . , X p−1 is E(Y |X 1 = x 1i , . . . , X p−1 = x p−1i ) = β 0 +β 1 x 1i +. . . +β p−1 x p−1i var(Y |X 1 = x 1...