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Stephanie Glen
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  • Jacksonville, FL
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Nikita liked Stephanie Glen's blog post Model evaluation techniques in one picture
15 hours ago
Arvin Hsu commented on Stephanie Glen's blog post Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests
"I think your link to the L^1 RMSE is incorrect.  Or at least there's no mention of the former in the latter article.  The article did describe a way to evaluate model fit that was more outlier robust, but didn't call it was based…"
Jul 10
Diana Wigzell liked Stephanie Glen's blog post Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests
Jul 10
Stephanie Glen's 2 blog posts were featured
Jul 10
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Jun 23
Stephanie Glen's blog post was featured

Comparing Classifiers: Decision Trees, K-NN & Naive Bayes

A myriad of options exist for classification. In general, there isn't a single "best" option for every situation. That said, three popular classification methods— Decision Trees, k-NN & Naive Bayes—can be tweaked for practically every situation.OverviewNaive Bayes and K-NN, are both examples of supervised learning (where the data comes already labeled). …See More
Jun 19
Stephanie Glen's blog post was featured

The Lifecycle of Data

The lifecycle of data travels through six phases:The lifecycle "wheel" isn't set in stone. While it's common to move through the phases in order, it's possible to move in either direction (i.e. forward, backward) at any stage in the cycle. Work can also happen in several phases at the same time, or you can skip over entire phases. In addition, if new information is…See More
Jun 18
Stephanie Glen's 2 blog posts were featured
Jun 15
Stephanie Glen's blog post was featured

Alternatives to R-squared (with pluses and minuses)

R-squared can help you answer the question "How does my model perform, compared to a naive model?". However, r2 is far from a perfect tool. Probably the main issue is that every data set contains a certain amount of unexplainable data. R-squared can't tell the difference between the explainable and the unexplainable , so it will go…See More
Jun 10
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Stephanie Glen's 2 blog posts were featured
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Profile Information

Short Bio
Stephanie Glen is Editorial director at DataScienceCentral.com. She is a mathematician and statistician. After working for several years teaching mathematics and statistics at the university level, she created and developed the website StatisticsHowTo.com.
Field of Expertise
Other
Professional Status
Executive
Years of Experience:
14
Your Company:
Andale Publishing, LLC
Industry:
Mathematics Education
Your Job Title:
CEO
Interests:
Contributing

Stephanie Glen's Blog

Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests

Posted on July 10, 2019 at 5:36am 1 Comment

Evaluating a model is just as important as creating the model in the first place. Even if you use the most statistically sound tools to create your model, the end result may not be what you expected. Which metric you use to test your model depends on the type of data you’re working with and your comfort level with statistics.

Model evaluation techniques answer three main questions:

  1. How well does your model match your data (in other words, what is the…
Continue

Model evaluation techniques in one picture

Posted on June 29, 2019 at 7:38am 0 Comments

The sheer number of model evaluation techniques available to asses how good your model is can be completely overwhelming.  As well as the oft-used confidence intervals, confusion matrix and…

Continue

Comparing Classifiers: Decision Trees, K-NN & Naive Bayes

Posted on June 19, 2019 at 6:49am 0 Comments

A myriad of options exist for classification. In general, there isn't a single "best" option for every situation. That said, three popular classification methods— Decision Trees, k-NN & Naive Bayes—can be tweaked for practically every situation.

Overview

Naive Bayes and K-NN, are both examples of supervised learning (where the…

Continue

Assumptions of Linear Regression in One Picture

Posted on June 15, 2019 at 7:53am 0 Comments

If any of the main assumptions of linear regression are violated, any results or forecasts that you glean from your data will be extremely biased, inefficient or misleading. Navigating all of the different assumptions and recommendations to identify the assumption can be overwhelming (for example, normality has more than half a dozen options for testing).
This image highlights the assumptions and the most common testing options.…
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