EVALUATION METRICS FOR CLASSIFIERS

Hello, welcome to my blog. In my previous posts I introduced the concept of classification. I talked about the operation of a linear classifier, how to learn the coefficients for a linear classifier using logistic regression and I demonstrated how to implement logistic regression in Python.

In this post I want to talk about how we evaluate a classifier.  Concretely, how do we know if a classifier is doing good or poorly on data? This question is the theme of this post.

IMPLEMENTING LOGISTIC REGRESSION IN PYTHON

Hello there. Welcome to my blog. In the last post I talked about the algorithm used to learn coefficients for linear classifiers – logistic regression. In case you have not read it, click here. 

In this post, I want to demonstrate how to implement logistic regression using the sci-kit learn library in Python. I will show the full program at the end of the post, here I will just display the screenshots of the results of the program.

LOGISTIC REGRESSION

Hello, welcome to my blog. In my previous posts I introduced the concept of classification and I also described the operation of a linear classifier. Basically a linear classifier computes a score for an input and classifies the input to a class based on the value of that score.

LINEAR CLASSIFIERS

Hello, welcome to my blog. In the previous post I talked about classification and its popular applications in the real world. I also listed the popular algorithms used for classification. Now I want to introduce the concept of a linear classifier. For this post, I will use a restaurant review system as a practical case study to make the concept of linear classifiers clear.