Was I Wrong About the Death of Data Science?

Well, that didn’t take long.

In response to my previous post, two resounding replies immediately returned: AutoML and Cloud AI/ML APIs. While I thought I had addressed these indirectly, apparently I could have been more clear as to these specific topics…

Feature Expansion and Linear Classifiers

Let’s start with an impossible problem. You are trying to use a linear classifier to classify data into “Red” and “Blue” categories. You have only eight data points, and the data have the following distribution:

The Perceptron meets its match

Of course, we don’t know the distribution ahead of time…

Intentionally Creating and Then Remediating Bias using Boosting

One old con goes something like this: approach a healthy person and convince them they have a malady based on symptoms that universally apply to nearly every human being. For example: “Do you sweat when you run? Does it get worse if…

Decision Tree Models Using Bagging and Random Forest

In my previous post I discussed the problem of variance — defined as fitting a machine learning model to training data that doesn’t accurately reflect reality very well. This “overfitting” to training data produces great results when making predictions on that same…

The Promise and Challenge of Synthetic Health Data

It is hard to overstate the potential for machine learning / data science to dramatically impact healthcare outcomes. The potential value in terms of better treatments, lives saved, higher quality of life, less expensive care, reduced load on clinical staff, and other…

Jason Eden

Data Science, Big Data, & Cloud nerd with a focus on healthcare & a passion for making complex topics easier to understand. All thoughts are mine & mine alone.

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