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Data Science Blog

Machine learning & data science for beginners and experts alike.
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Alteryx Alumni (Retired)

Many if not most supervised-classification problems involve some degree of class imbalance, where at least one class occurs more frequently than the others.  The imbalanced-classification problem illustrates the value of approaching data-science problems as empirical (as well as formal) optimization problems, using techniques termed cost-sensitive learning. This post will show you how to do cost-sensitive binary classification.

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Alteryx Alumni (Retired)

Most real-world data-science design patterns combine several models to solve a single business problem.  This post surveys the most common and effective techniques for combining models.  Once you make it through this post (and its predecessors), you'll be ready to take on the design patterns we'll begin learning in 2017. 

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Alteryx Alumni (Retired)

Cross validation (CV) is a difficult topic.  There are many ways to do CV, and articles on the subject can be very technical.  This blog post is a gentle introduction to CV.  Read it and you'll find it much easier to understand later posts describing data-science design patterns that use CV. 

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Alteryx Alumni (Retired)

Understanding fitting algorithms is the final hurdle between you and some juicy data science design patterns.  Make the leap! 

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Alteryx Alumni (Retired)

Nothing is more stylish than a well-fitted induction algorithm.

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Alteryx Alumni (Retired)

An advanced-analytics model's induction algorithm is its prediction engine.  Learn what induction-algorithm features distinguish parametric statistical models and machine-learning models.

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Alteryx Alumni (Retired)

Learn why "all models are wrong, but some models are useful."

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Alteryx Alumni (Retired)

Citizen data scientist?  Learn design patterns that let you use Alteryx to build data-science expertise into your data analyses and analytical models!

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