Top 10 Features to Look for in Automated Machine Learning
Following best practices when buildingmachine learning modelsis a time-consuming yet important process. There are so many things to do ranging from: preparing the data, selecting and training algorithms, understanding how the algorithm is making decisions, all the way down to deploying models to production. I like to think of the machine learning design and maintenance process as being comprised of ten steps (see the diagram above).
But, if I want to save time, increase accuracy, and reduce risk, I don’tmanuallygo through the entiremachine learning processin order to build my machine learning models. Instead, I turn toautomated machine learning, using clever software that knows how to automate the repetitive and mundane steps, and freeing me up to do what humans are best at: communication, applying common sense, and being creative. And, to get the most out of automated machine learning, I want it to automate each and every one of the 10 steps (see diagram above). So, here’s my guide to what to look for in an automated machine learning system.
A data mining technique that involves transforming raw data into an understandable format.
Eachalgorithmworks differently and has different data requirements. For example, some algorithms need numeric features to be normalized, and some do not. Then there’s the complication of text, which needs to be split into words and phrases, and in some languages, such as Japanese, that’s really difficult!
Look for an automated machine learning platform that knows how tobest prepare data for each different algorithm, recognizes and prepares text, and follows best practice for data partitioning.
Feature engineeringis the process of altering the data to help machine learning algorithms work better, which is often time-consuming and expensive. While some feature engineering requires domain knowledge of the data and business rules, most feature engineering is generic.
Look for an automated machine learning platform that canautomatically engineer new featuresfrom existing numeric, categorical, and text features. You will want a system that knows which algorithms benefit from extra feature engineering and which don’t, and only generates features that make sense given the data characteristics.
Every dataset contains unique information that reflects the individual events and characteristics of a business. Due to the variety of situations and conditions,one algorithm cannot successfully solve every possible business problem or dataset. Because of this, we need access to a diverse repository of algorithms to test against our data, in order to find the best one for our particular data.
Look for an automated machine learning platform that has dozens, or even hundreds of algorithms. Ask how often new algorithms are added.
Having hundreds of algorithms available at your fingertips is great, but unless you are more patient than I am, you don’t have time to try each and every one of those algorithms on your data. Some algorithms aren’t suited to your data, some are not suited to your data sizes, and some are extremely unlikely to work well on your data.
Look for an automated machine learning platform thatknows which algorithms make sense for your dataand runs only those. That way you will get better algorithms, faster.
It’s quite standard for machine learning software to train the algorithm on your data. After all, you wouldn’t want to manually doNewton-Raphson iterationwould you? Probably not. But, often there’s still the hyperparameter tuning to worry about. Then you want to do feature selection, to improve both the speed and accuracy of a model.
Look for an automated machine learning platform that uses smart hyperparametertuning, not just brute force, and knows the most important hyperparameters to tune for each algorithm.
