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ML Best Practices

Machine learning is a complex and multifaceted activity that requires the combination of a number of success factors in order to work. In order to execute machine learning well, it is important to have a good understanding of the processes and variables that frequently come into play together. Here I document and lay out some of the best practices and ML processes that I have learned and continue to learn during my career.

A personal bugbear of mine is when people refer to machine learning as "an art rather than a science". It is true that machine learning is non-deterministic and therefore there are a number ofinvolves random-seeming aspects that could be conflated with magic or wizardry. There are a lot of ways that poorly thought-through or executed machine learning projects can go wrong. However, there are well established protocols and principles that can guide a project towards success and we can followexecute which provide intuition about why an ML model might or might not perform wellprojects in certaina contexts.risk-averse, incremental way allowing us to fail fast before a large amount of time and energy has been spent on a project.

Key Principles

Garbage In; Garbage Out

Machine learning is all about trying to infer statistical relationships between data. There are a lot of reasons why this might not work well which we will explore in depth. It's always important to remember that if you have bad data or a flawed hypothesis, your model is not going to perform well. Conversely, if your model is performing really well (perhaps "too good to be true" well) this can be a red flag that something is wrong with your data or your process which leads into...

Always Be Questioning

If something appears to be too good to be true, it probably is.is:

    Typically if a machine learning model is achieving high-90% in appropriate performance metrics, it is worth your time to investigate whether something weird is happening. You may have training data in your test set or your model may have found a short cut. For example, if all the positive examples of fractured ribs come from x-rays of women and all the negative examples come from x-rays of men then the model may have just learned to separate male and female anatomy rather than what a fractured rib looks like.

    Keep it SimpleSimple, Stupid (KISS)

    As tempting as it might be to want to use the latest and greatest ML models, always start with the simplest approach possible and disqualify these approaches based on sound theoretical rationale or empirical experimentation.experimentation:

      The more complex a modelling approach, the more uncertainty we invite into the process. Complex models are significantly more expensive to train and run and scaling them to run quickly on a large amount of data is a challenge. 

      Keep the Receipts

       

       

      Deep Learning Best Practices

      For deep learning I like to work with torch. My opinion on how torch code should be written aligns quite closely with this styleguide