If you don’t know what any of these are, Gradient Descent was explained in theLinear Regression post, and an explanation of Maximum Likelihood for Machine Learning can be found here: Probability Learning III: Maximum Likelihood Another step in our way to become probability masters… ...
神经网络基础篇:详解logistic 损失函数(Explanation of logistic regression cost function) 详解logistic 损失函数 在本篇博客中,将给出一个简洁的证明来说明逻辑回归的损失函数为什么是这种形式。 回想一下,在逻辑回归中,需要预测的结果^yy^,可以表示为^y=σ(wTx+b)y^=σ(wTx+b),σσ是熟悉的SS型函数 σ(z...
Logistic regression is a popular data analytic technique, but a compelling rationale for the equations that appear is missing in the conventional explanations. However, if one approaches logistic regression from a combined Bayesian and Maximum Entropy viewpoint, the explanation of its origin is relative...
Interpretation of Regression Coefficients The interpretation of the estimated regression coefficients is not as easy as in multiple regression. In logistic regression, not only is the relationship between X and Y nonlinear, but also, if the dependent variable has more than two unique values, there ...
Deep Learning with Theano - Part 1: Logistic RegressionOver the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image ...
Motivation One of the most common comments I hear is that logistic regression (also called Binomial regression) is some kind of “advanced magic”, “machine learning”, “artificial intelligence” or “big data”. This is not true. In this post, I will
Thank you for this detailed explanation/tutorial on Logistic Regression. I have few queries related to Logistic Regression which I am not able to find answers over the internet or in books. It would be of great help if you could help me understand these uncleared questions. ...
Now, let me briefly explain how that works and how softmax regression differs from logistic regression. I have a more detailed explanation on logistic regression here:LogisticRegression - mlxtend, but let me re-use one of the figures to make things more clear: ...
The explanation of the beta vector update process of the Newton-Raphson algorithm presented in this article and the accompanying code download should get you up and running with logistic regression using NR. Logistic regression is a fascinating, complex topic and can make a valuable addition to you...
While the examples I'll use here only have measurement variables as the independent variables, it is possible to use nominal variables as independent variables in a multiple logistic regression; see the explanation on the multiple linear regression page....