Posts tagged Logistic Regression

MNIST – Regularized Logistic Regression

Hello guys


Sometimes when we train our algorithm, it becomes too specific to our dataset which is not good. Why? Because the algorithm must be able to classify correctly data never seem before too. So today, I’ll show you a way to try to improve the accuracy of our algorithm.

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Kaggle MNIST competition


Last post we classified MNIST using our classifier. I was wondering how we could evaluate our algorithm with “real” data. What if we could evaluate it against others algorithms from other people?

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Multi-Class classification with Logistic Regression


Until now our algorithm was able to perform binary classification, in other words it could only classify one thing among several other stuffs.  I was wondering whether it would be nice to improve our algorithm to be a multi-class classifier and classify images with it.

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Logistic Regression – Hands on


Today we’ll get hands dirty and test logistic regression algorithm. For this post, we are going to use the very known iris flower data set.This dataset has three classes of flowers which can be classified accordingly to its sepal width/length and petal width/length. From the dataset source “One class is linearly separable from the other 2 […]” which makes this dataset handy for our purposes of binary classification.

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Logistic Regression

Hi folks,
Yeah, things are getting more interesting, huh? In the last posts we covered linear regression where we fit a straight line to represent the best way possible a set of points. This is a simple and powerful way to predict values. But sometimes instead of predicting a value, we want to classify them.

Why we would do that? I know that is easy to understand but for those who didn’t catch it, why this is interesting?

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