Machine Learning Packages In R

Khushbu Shah discusses good R packages to help with your machine learning projects:

If missing values are something which haunts you then MICE package is the real friend of yours.

When we face an issue of missing values we generally go ahead with basic imputations such as replacing with 0, replacing with mean, replacing with mode etc. but each of these methods are not versatile and could result into a possible data discrepancy.

MICE package helps you to impute missing values by using multiple techniques, depending on the kind of data you are working with.

I’d heard of a couple of these, but most of them are new to me.

Related Posts

Housing Prices In Ames, Iowa: A Kaggle Competition

Kathryn Bryant and M. Aaron Owen share their Kaggle experiences.  First, Kathryn, et al: The lifecycle of our project was a typical one. We started with data cleaning and basic exploratory data analysis, then proceeded to feature engineering, individual model training, and ensembling/stacking. Of course, the process in practice was not quite so linear and […]

Read More

Data Wrangling At Scale

John Mount has a short article showing off the cdata package: Suppose we needed to un-pivot this data into a row oriented representation. Often big data transform steps can achieve a much higher degree of parallelization with “tall data”. With the cdata package this transform is easy and performant, as we show below. Read the whole thing.

Read More


June 2016
« May Jul »