- Idiot’s Guide to Precision, Recall, and Confusion Matrix - Jan 13, 2020.
Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.
- Top KDnuggets tweets, Dec 11-17: Idiot’s Guide to Precision, Recall and Confusion - Dec 20, 2019.
Idiot's Guide to Precision, Recall and Confusion Matrix; 10 Free Must-Read Books for Machine Learning and Data Science; How to Speed up Pandas by 4x with one line of codes; #Math for Programmers teaches you the math you need to know.
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
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- Dealing with Unbalanced Classes, SVMs, Random Forests, and Decision Trees in Python - Apr 29, 2016.
An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.
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- 21 Must-Know Data Science Interview Questions and Answers - Feb 11, 2016.
KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.
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- How to Balance the Five Analytic Dimensions - Sep 3, 2015.
When developing a solution one has to consider data complexity, speed, analytic complexity, accuracy & precision, and data size. It is not possible to best in all categories, but it is necessary to understand the trade-offs.