Learn to use Forward Selection Techniques for Ensemble Modeling

Ensemble methods have the ability to provide much needed robustness and accuracy to both supervised and unsupervised problems. Machine learning is going to evolve more and more and computations power becomes cheap and the volume of data continues to increase. In such a scenario, there is a limit to the improvement you can achieve by using a single framework and attempting to improve its predictive power (using modification in variables). Ensemble Modeling follows the philosophy of ‘Unity in Strength’ i.e. combination of diversified base models strengthens weak models. The success of ensemble techniques spreads across multiple disciplines like recommendation systems, anomaly detection, stream mining, and web applications where the need for combination of competing models is ubiquitous.

Logistic Regression in R – Part Two

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to produce the most useful model. While the following content isn’t exhaustive, it should provide a compact ‘cheat sheet’ and guide for the modeling process.

Mathematical annotations on R plots

I’ve always struggled with using plotmath via the expression function in R for adding mathematical notation to axes or legends. For some reason, the most obvious way to write something never seems to work for me and I end up using trial and error in a loop with far too many iterations. So I am very happy to see the new latex2exp package available which translates LaTeX expressions into a form suitable for R graphs. This is going to save me time and frustration!

How do you know if your model is going to work? Part 1: The problem

Here’s a caricature of a data science project: your company or client needs information (usually to make a decision). Your job is to build a model to predict that information. You fit a model, perhaps several, to available data and evaluate them to find the best. Then you cross your fingers that your chosen model doesn’t crash and burn in the real world.

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