**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.

Tags: Accuracy, Beginners, Classification, Precision, Predictive Modeling, Recall

**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.

Tags: Precision, Recall, Top tweets

**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.

**Pages:** 1 2

Tags: Accuracy, Classification, CleverTap, Measurement, Metrics, Precision, Unbalanced

**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.

**Pages:** 1 2 3

Tags: Balancing Classes, Decision Trees, Precision, Python, Recall, Support Vector Machines, SVM, Unbalanced

**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.

**Pages:** 1 2 3

Tags: Bootstrap sampling, Data Science, Interview questions, Kirk D. Borne, Precision, Recall, Regularization, Yann LeCun

**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.

Tags: Accuracy, Complexity, Precision