(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...
J_history=np.zeros((num_iters,1))foriterinrange(num_iters):# 对J求导,得到 alpha/m*(WX-Y)*x(i),(3,m)*(m,1)X(m,3)*(3,1)=(m,1)theta=theta-(alpha/m)*(X.T.dot(X.dot(theta)-y))J_history[iter]=computeCost(X,y,theta)returnJ_history,theta iterations=10000#迭代次数 alph...
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] *...
梯度下降算法还受到Learnin rate的影响,如果 过小,收敛速度会非常慢,需要迭代很多次,如果 过大,迭代可能使代价函数不收敛跳过局部最优值。 通常可以尝试以下的Learning rate: ..., 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1, ... 5. Features and polynomial regression 这里讲解多项式回归。对于线性回归可能并...
多特征 Gradient Descent for Multiple Variables 多变量代价函数相对于单变量代价函数,没有什么变化。唯一变化就在于,原先是一个数,现在成了一个矩阵的数。公式等没有其他变化。 Gradient Descent in Practic...吴恩达机器学习(二)——单变量线性回归(Linear Regression with One Variable) 1、模型表示 在监督学习...
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
[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 ...
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...
Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.
Linear regression and multiple regression are two types of regression analysis. Key Takeaways Regression analysis is a statistical method used in finance and investing. Linear regression (also called simple regression) contains only two variables: the independent variable and the dependent variable....