Posts by Tyler Schlosser

May 7, 2018 by

Machine Learning: Is it really a Black Box?

Machine Learning isn’t the “black box” that many perceive it to be. On complex data sets, the use of Machine Learning with a rigorous process and supporting visualizations can yield far more transparency than other methods. What is a “Black Box”? Machine learning models are sometimes characterized as being Black Boxes due to their powerful ability to model complex relationships between inputs and outputs without being accompanied by a tidy, intuitive description of how exactly they do this. A “Black Box” is “a device, system or object which can be viewed in terms of inputs and outputs without any knowledge of its internal workings” (Source: Wikipedia). Black Boxes (and Machine Learning models) exist everywhere We tend to label things as “Black Boxes” when we don’t trust them more than when we don’t understand them. Machine Learning models aren’t unique in having an element of “mystery” in how they work – there are all sorts of things we trust all around us for which we don’t fully understand the inner workings. GPS, search engines, car engines, step counters, even the curve fitting algorithms in Excel are examples where we trust what’s happening inside because we’re able to see and, with experience,...

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March 2, 2018 by

Machine Learning: Finding the signal or fitting the noise?

Before machine learning came along, a typical approach to building a predictive model was to develop a model that best fit the data. But will a model that best fits your data provide a good prediction? Not necessarily. Fortunately, there are machine learning practices that can help us estimate and optimize the predictive performance of models. But before we delve into that, let’s illustrate the potential problem of “overfitting” your data. Fitting the Trend vs. Overfitting the Data For a given dataset, we could fit a simple model to the data (e.g., linear regression) and likely have a decent chance of representing the overall trend. We could alternatively apply a very complex model to the data (e.g. a high-degree polynomial) and likely “overfit” the data – rather than representing the trend, we’ll fit the noise. If we apply the polynomial model to new data, we can expect it to make poor predictions given it’s not really modeling the general trend. The example above illustrates the difference between modelling the trend (the red straight line) and overfitting the data (the blue line). The red line has a better chance of predicting values outside of the dataset presented. Due to the powerful...

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