(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 N
(2)损失函数和单变量一样,依然计算损失平方和均值 我们的目标和单变量线性回归问题中一样,是要找出使得代价函数最小的一系列参数。多变量线性回归的批量梯度下降算法为: 求导数后得到: (3)向量化计算 向量化计算可以加快计算速度,怎么转化为向量化计算呢? 在多变量情况下,损失函数可以写为: 对theta求导后得到: (1...
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] *...
【吴恩达机器学习笔记】004 模型示例:单变量线性回归(Model Representation:Linear Regression with one variable) 一、示例 —— 预测住房价格 如上图所示,图中的数据集(X)表示房屋大小和价格存在的一个关系图。如果有一个人有一套1250平方尺的房子,那他卖多少钱合适? 二、分析 或许我们每个人心中... ...
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
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...
resulting in a new average value for the input variable of just zero. Gradient Descent in Practice ML学习笔记 (2) Multivariate linear regression more powerful one that works with multiple variables or with multiple features linear regression. n = number of features = input (features) of ...
Linear regression with multiple variables(多特征的线型回归)算法实例_梯度下降解法(Gradient DesentMulti)以及正规方程解法(Normal Equation),%第一列为sizeofHouse(feet^2),第二列为numberofbedroom,第三列为priceofHouse12104,3,39990021600,3,32990032400,3,3690004
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...
In general, suppose the categorical variable has k levels ( k\geq2 ) We only need to define k-1 indicator variables. Define: x_1=\begin{cases}1&level1\\0&otherwise\end{cases},...,x_{k-1}=\begin{cases}1&level(k-1)\\0&otherwise\end{cases} Level k is the baseline. The mul...