An interesting development occured in the Job salary prediction at Kaggle: the guy who ranked 3rd used logistic regression, in spite of the task being regression, not classification. We attempt to replicate the experiment.

There’s a contest at Kaggle held by Qatar University. They want to be able to discriminate men from women based on handwriting. For a thousand bucks, well, why not?

Last time we explored dimensionality reduction in practice using Gensim’s LSI and LDA. Now, having spent some time researching the subject matter, we will give an overview of other options.

The job salary prediction contest at Kaggle offers a highly-dimensional dataset: when you convert categorical values to binary features and text columns to a bag of words, you get roughly 240k features, a number very similiar to the number of examples.

We present a way to select a few thousand relevant features using L1 (Lasso) regularization. A linear model seems to work just as well with those selected features as with the full set. This means we get roughly 40 times less features for a much more manageable, smaller data set.

To celbrate the first 100 followers on Twitter, we asked them what would they like to read about here. One of the responders, Itamar Berger, suggested a topic: how to choose a ML algorithm for a task at hand. Well, what do we now?

Much of data in machine learning is sparse, that is mostly zeros, and often binary. The phenomenon may result from converting categorical variables to one-hot vectors, and from converting text to bag-of-words representation. If each feature is binary - either zero or one - then it holds exactly one bit of information. Surely we could somehow compress such data to fewer real numbers.

We’re back to Kaggle competitions. This time we will attempt to predict advertised salaries from job ads and of course beat the benchmark. The benchmark is, as usual, a random forest result. For starters, we’ll use a linear model without much preprocessing. Will it be enough?

Are you interested in linear models, or K-means clustering? Probably not much. These are very basic techniques with fancier alternatives. But here’s the bomb: when you combine those two methods for supervised learning, you can get better results than from a random forest. And maybe even faster.

Once upon a time we were browsing machine learning papers and software. We were interested in autoencoders and found a rather unusual one. It was called marginalized Stacked Denoising Autoencoder and the author claimed that it preserves the strong feature learning capacity of Stacked Denoising Autoencoders, but is orders of magnitudes faster. We like all things fast, so we were hooked.

Lately we’ve been working with the Madelon dataset. It was originally prepared for a feature selection challenge, so while we’re at it, let’s select some features. Madelon has 500 attributes, 20 of which are real, the rest being noise. Hence the ideal scenario would be to select just those 20 features.