Category: Data Science

Vincent Granville has an easy trick for removing serial correlation from a data set:

Here is a simple trick that can solve a lot of problems.

You can not trust a linear or logistic regression performed on data if the error term (residuals) are auto-correlated. There are different approaches to de-correlate the observations, but they usually involve introducing a new matrix to take care of the resulting bias. See for instance here.  

Click through for the alternative.

Stephanie Glen shows us cross-validation in one picture:

Cross Validation explained in one simple picture. The method shown here is k-fold cross validation, where data is split into k folds (in this example, 5 folds). Blue balls represent training data; 1/k (i.e. 1/5) balls are held back for model testing.

Monte Carlo cross validation works the same way, except that the balls would be chosen with replacement. In other words, it would be possible for a ball to appear in more than one sample.

You’ll have to click through for the picture.

Rahul Khanna continues a series on support vector machines:

In this blog post, we will look at a detailed explanation of how to use SVM for complex decision boundaries and build Non-Linear Classifiers using SVM. The primary method for doing this is by using Kernels.

In linear SVM we find margin maximizing hyperplane with features Xi’s . Similarly, in Logistic regression, we also try to find the hyperplane which minimizes logistic loss with features Xi’s. Most often when we use both these techniques the results are the same. But linear SVM or for the same reason a logistic regression would fail where there is a need to have complex or non-linear decision boundaries. These types of boundaries are then achieved by SVM using Kernels. So let us understand how SVM creates non-linear boundaries using Kernels

Read on to see how it works.

Bruno Stecanella explains the concept behind TF-IDF:

TF-IDF was invented for document search and information retrieval. It works by increasing proportionally to the number of times a word appears in a document, but is offset by the number of documents that contain the word. So, words that are common in every document, such as this, what, and if, rank low even though they may appear many times, since they don’t mean much to that document in particular.

However, if the word Bug appears many times in a document, while not appearing many times in others, it probably means that it’s very relevant. For example, if what we’re doing is trying to find out which topics some NPS responses belong to, the word Bug would probably end up being tied to the topic Reliability, since most responses containing that word would be about that topic.

This makes the technique useful for natural language processing, especially in classification problems.