Using correlation to get the most out of your data and modeling

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Photo by unsplash.com/alinnnaaaa

In my last post, I discussed the Gini impurity metric, and how it can assist your work in fitting a decision tree model. Knowledge of mathematics, however, is not limited to interpreting the results and performance of a machine learning algorithm. More importantly, there is no concept of data science that is not worth revisiting or delving more in depth on. We are often attracted to new and exciting projects and challenges, but sometimes it is worthwhile to return to the basics we thought we already understood. That is the drive of this article; I hope to discuss a concept…


Using Gini impurity to your advantage in Decision Tree Classifiers

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Photo by unsplash.com/@andrewtneel

This article will serve as the first part of a potentially ongoing series, looking at the mathematical concepts that drive key parameters in the machine learning algorithms employed in data science. My goal in these posts will be to express key concepts in as simple and non-technical a language as possible, while not sacrificing any critical precision to the concepts themselves. As any good data scientist knows, there is far more to understanding your data than importing an estimator from sklearn or statsmodels and typing .fit() in your script, even if sometimes the most experienced of data scientists can make…


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Image Source from Phillips.com

In this article, I discuss my most recent data science project. Using x ray images as data, I investigate the possibilities, pitfalls, and limitations of using machine learning algorithms as an assistant to a radiology team.

Medical imaging is a vital and widely used diagnostic tool in clinical health care, allowing physicians to get a sense of abnormalities in a patient’s anatomy and physiology that would otherwise be difficult or impossible to confirm. Rapidly advancing technology in the field leads to ever increasing use and expansion of medical imaging to improve the quality of patient care. However, with all of…


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An illustration of a typical CNN used in image classification

Deep learning applications in image classification have shown very impressive results in recent years, with advances rapidly happening all the time. They are being applied in many fields, such as: security and facial recognition, diagnostic support in medical imaging, and the development of driverless car technologies. This makes deep learning an increasingly valuable skill set for data scientists to be familiar with. If you have worked extensively with data, and especially if you have built neural networks before and are familiar with keras, but have not attempted a thorough image classification project before, this article is for you!

Much of…

John Lawless

I am a data scientist with a wide professional background, from artistry to clinical healthcare, all of which I utilize to find nuances in our data rich world.

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