The Sigmoid Function in Logistic Regression | iPython Notebooks
The Sigmoid Function in Logistic Regression¶In learning about logistic regression, I was at first confused as to why a sigmoid function was used to map from the inputs to the predicted output. I mean, sure, it's a nice function that cleanly maps from any real number to a range of $-1$ to $1$, but where did it come from? This notebook hopes to explain. Logistic Regression¶With classification, we ha
Fun with machine learning: logistic regression!
November 17, 2014 As much as I write about how there’s a lot more to ML than training models, the actual statistical models you use are still really important! And I find understanding the models I’m using helps me sleep a lot better at night. So we’re going to talk about one. I only do classification right now. Classification means that you have a bunch of data with features (like (height = 5', w
Logistic Ordinal Regression
⊕ By Fabian Pedregosa. Category: misc #machine learning #ordinal regression #Python #ranking Thu 02 May 2013 TL;DR: I've implemented a logistic ordinal regression or proportional odds model. Here is the Python code The logistic ordinal regression model, also known as the proportional odds was introduced in the early 80s by McCullagh [1, 2] and is a generalized linear model specially tailored for t
Numerical optimizers for Logistic Regression
⊕ By Fabian Pedregosa. Category: misc #machine learning #logistic regression #Python #SciPy Mon 20 May 2013 In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization tools from scipy.optimize and compare them against state of the art implementations such as LIBLINEAR. In this blog post I'll write down all
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