# Diving into H2O with R

Do you understand the pain when you have to train advanced machine learning algorithms like Random Forest on huge datasets? When there is a factor column that has way too many number of levels? When the time taken to train the model is so huge that you went to your ...

# An illustrated introduction to adversarial validation part 2

In the last post we talked about the idea of adversarial validation and how it helps the problem when your validation set result doesn't comply with that of test set result. In this post, I will share the R code to help achieve the idea of adversarial validation. The ...

# How to use Git and Github

I had taken this course - How to use git and github some time last year. This post is an amalgamation of the course notes and other tutorials I have completed in understanding git. I will talk about the most frequently used commands. If you already are confident of your git ...

# An illustrated introduction to adversarial validation part 1

You'd have heard about cross-validation - a common technique used in data-science process to avoid overfitting and many a times to tune the optimal parameters. Overfitting is when the model does well on training data but fails drastically on test data. The reason could be one of the following:

1. The ...

# The curse of bias and variance [draft]

Statistics is the field of study where we try to draw conclusions about the population from a sample. Why do we talk about sample? Why can't we get the conclusions about the population directly from the population? Let me illustrate this by an example.

Let us say we want ...

# Visualization in ML is under-rated

Visualization is one of the most important pillars of data science. Every one wants to learn Machine learning but if you explain them the little tasks that involve the overall workflow of the process, it turns them off. Everyone just wants to do the cool stuff. They want to build ...

# Random Forest explained intuitively

Random Forests algorithm has always fascinated me. I like how this algorithm can be easily explained to anyone without much hassle. One quick example, I use very ...

# Improve runtime of Random Forest in R

There are two ways one can write the code to train a random forest model in R. Both the ways are listed below.

A normal and frequent way of writing the command to train the random forest model is something like this.

rfModel <- randomForest(Survived~. , data = trainSample[, -c(6, 8 ...

# How to install a package of a particular version in R

I recently tried installing caret package in R using

install.packages('caret', dependencies=T)


Normally this installation of package works and I continue to work with the functions associated with the package. When I tried including the package using

library(caret)


I got the following error.

Error in loadNamespace(j ...

# Shell commands come in handy for a data scientist

I am no expert of shell commands. I have been using them for quite some time and thought I give an attempt to list down the most common commands. I am writing these mostly from the perspective of a data-science guy. Let us get started.

I will use the file- ...