Methods To Improve Model Accuracy

Tristan Robinson shows how to go back to the drawing board when your model’s accuracy isn’t cutting it:

One of the reoccurring principles that appears with machine learning is that of Ockham’s razor, which states that the best models are simple models that fit the data well; this is not an irrefutable principle of logic, but a preference for simplicity. Therefore there is a need of balance between accuracy and simplicity to limit the feature set which tends to lead to better predictions. Simpler models are also more interpretable to humans which also helps. While the data I was working with was limited to around 35 features, there are many data science problems which have thousands of features and so this technique is even more crucial.

There are multiple methods to perform feature selection, of which a few will be covered here. The first method is greedy backward selection which starts with all the features and then finds the feature that hurts predictive power the least when removed, and you remove it. This is done iteratively until a point is met (which will be discussed later). Its known as greedy since it never looks back after removing the feature each time.

An alternative method is greedy forward selection which is basically the inverse, starts with no features, and looks for the feature that by itself is the best model. This then carries on in a similar vein to the backward selection but adding features. The point at which you stop with forward selection is that of diminishing returns for your accuracy.

Read the whole thing.  This is explanation rather than demonstration, but the explanation applies to pretty much any implementation you’re using.

Related Posts

The Microsoft Team Data Science Process Lifecycle Versus CRISP-DM

Melody Zacharias compares Microsoft’s Team Data Science Process lifecycle with the CRISP-DM process: As I pointed out in my previous blog, the TDSP lifecycle is made up of five iterative stages: Business Understanding Data Acquisition and Understanding Modeling Deployment Customer Acceptance This is not very different from the six major phases used by the Cross […]

Read More

Exploratory Analysis With Hockey Data In Power BI

Stacia Varga digs into her hockey data set a bit more: Once I know whether a variable is numerical or categorical, I can compute statistics appropriately. I’ll be delving into additional types of statistics later, but the very first, simplest statistics that I want to review are: Counts for a categorical variable Minimum and maximum […]

Read More


February 2018
« Jan Mar »