AI News, A Data-Driven Approach to Choosing Machine Learning Algorithms

A Data-Driven Approach to Choosing Machine Learning Algorithms

There is no best machine learning algorithm or algorithm parameters.

In this post, I want to encourage you to break free of this mindset and take hold of a data-driven approach that is going to change they way you approach machine learning.

Typically this power comes at a cost of difficulty to implement, the need for very large datasets, limited scalability, or a large number of coefficients that may result in over-fitting.

There are general classes of problems, say supervised problems like classification and regression and unsupervised problems like manifold learning and clustering.

You can map algorithms to classes of problems, for example, there are algorithms that can handle supervised regression problems and supervised classification problems, and both types of problems.

If you believe this statement is true, then reading about algorithm races in papers and blogs does not inform you about which algorithm to try on your problem.

New sets of algorithm configurations are essentially new instances of algorithms for you to challenge your problem (albeit, relatively constrained or similar in the results they can achieve).

want you to challenge this, to consider abandoning heuristics and best practices and take on a data-driven approach to algorithm selection.

Become the objective scientist, leave behind anecdotes and study the intersection of complex learning systems and data observations from your problem domain.

You develop trust by selecting the test options in a data-driven manner that gives you objective confidence that your chosen configuration is reliable.

The type of estimation method (split, boosting, k-fold cross validation, etc.) and it’s configuration (size of k, etc.).

If random forest is your favorite algorithm, you could spend days or weeks trying in vain to get the most from the algorithm on your problem, which may not be suited to the method in the first place.

The result is that you no longer care about algorithm hype, it’s just another method to include in your spot checking suite.

You no longer fret over whether you’re missing out by not using algorithm X or Y or configuration A or B (fear of loss), you throw them in the mix.

You can write a reusable script to try automatically 10, 20, 100 algorithms across a variety of libraries and implementations.

We yearn for silver bullet general purpose best algorithms and best algorithm configurations, when no such things exist.

We must take a data-driven problem, to spot check algorithms, to grid search algorithm parameters and to quickly find methods that yield good results, reliably and fast.

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