After the basics of Regression, it’s time for basics of Classification. And, what can be easier than Logistic Regression!

This is what Classification actually means:

It’s time to give the Algorithm series, an informative start. Here, we start with one of the most famous category i.e. Tree Based Models, which consists of Decision Trees, Random Forest and Boosting methods.

In our 12A12D series, we are trying to be as diverse as possible. I can bet that 90% of you are unaware of ARM Techniques, even though they are basics of Data Mining.

After completing 4 Algos of our 12 Algos in 12 Days series, here is one of the most popular and most talked about Algorithm since its development. Best thing I like about SVM is that it’s completely based on Mathematical Optimization problem.

Let’s look at it’s simplicity here:

I know it’s getting arduous to catch up with out daily posts, but it’s just meant to be this. 12 Algos in 12 days.
Here, we unbox one of the most powerful ML technique used by Grandmasters to win Data Hackathons on Kaggle. You’ll consider yourself lucky if you understand this properly.

In our previous post on Decision Trees, we introduced the world of Tree-Based Modeling to you. Here, we extend that to explain a new concept known as Bagging or Bootstrap Aggregating.

To start off, here are some bags for you: