<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Performance Metrics for Classification Algorithms on Amit Rajan</title><link>https://amitrajan012.github.io/topics/performance-metrics-for-classification-algorithms/</link><description>Recent content in Performance Metrics for Classification Algorithms on Amit Rajan</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 29 Oct 2018 12:11:37 +0100</lastBuildDate><atom:link href="https://amitrajan012.github.io/topics/performance-metrics-for-classification-algorithms/index.xml" rel="self" type="application/rss+xml"/><item><title>Performance Metrics for Classification Algorithms</title><link>https://amitrajan012.github.io/post/performance-metrics-for-classification-algorithms/</link><pubDate>Mon, 29 Oct 2018 12:11:37 +0100</pubDate><guid>https://amitrajan012.github.io/post/performance-metrics-for-classification-algorithms/</guid><description>&lt;p&gt;There are several metrics that can be used to measure the performance of a classification algorithm. The choice for the same depends on the problem statement and serves an important role in model selection.&lt;/p&gt;&#10;&lt;/br&gt;&#10;### Confusion Matrix :&#10;&lt;p&gt;&lt;b&gt;Confusion matrix&lt;/b&gt; is one of the easiest and the most intutive way to find the correctness and accuracy of the model. It serves as the building block for all the other performance measures. A sample confusion matrix is shown below:&lt;/p&gt;</description></item></channel></rss>