Hello, welcome to my blog. It’s been a while since my last post which is mostly due to some personal projects I have being doing, laziness :) and other factors. Anyway, I want to introduce another project I did for the Statistics with R specialization in this post. You can see it by following this link.
Thursday, 4 August 2016
Saturday, 30 July 2016
Hello, welcome to my blog. I know it’s been long since my last post – I apologize for that. I have been quite busy for the past few weeks with some projects and I have not had any time to write. One of the things that has kept me busy is some of the courses I have been taking on Coursera – particularly the Statistics with R specialization. In this post, I will present the project I did for one of the courses of this specialization.
Sunday, 3 July 2016
Hello, welcome to my blog. Apologies for the delay in writing this post, I have been a little preoccupied lately. Thankfully I am able to create time to write this post. In this post, I am going to address the problem of distinguishing images that are ads from non-ads. Concretely, given an image the goal is to determine if it’s an advertisement (“ad”) or not an advertisement (“non-ad”). I am going to use the R programming language for this demonstration.
Sunday, 12 June 2016
Hello, welcome to my blog. In my previous post I introduced the concept of ensemble classifiers. I also talked about their operation and two popular ensemble methods – Boosting & Random Forests.
In this post I want to demonstrate how to implement the two ensemble methods mentioned above using the GraphLab library in Python. I will use the same dataset – LendingClub dataset so we can compare the performance of the single tree model to the ensemble model.
Monday, 30 May 2016
Hello, welcome to my blog. Recently, I have been talking about two algorithms for classification namely logistic regression & decision trees. I also demonstrated how we can implement these algorithms using Python’s scikit-learn library.
Today, I want to talk about Ensemble classifiers. The fundamental idea behind ensemble classifiers is combining a set of classifiers to make one better classifier. Concretely, an ensemble classifier combines two or more classifiers (also called a weak learner or classifier) in order to make a stronger classifier (also called a strong learner or classifier).