<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://atrcheema.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="https://atrcheema.github.io/" rel="alternate" type="text/html" /><updated>2026-08-02T06:01:58-07:00</updated><id>https://atrcheema.github.io/feed.xml</id><title type="html">Ather Abbas</title><subtitle>PhD student in Environmental Engineering</subtitle><author><name>Ather Abbas</name></author><entry><title type="html">What is scikit-learn?</title><link href="https://atrcheema.github.io/posts/2022/09/blog-post-1/" rel="alternate" type="text/html" title="What is scikit-learn?" /><published>2012-09-11T00:00:00-07:00</published><updated>2012-09-11T00:00:00-07:00</updated><id>https://atrcheema.github.io/posts/2022/09/blog-post-1</id><content type="html" xml:base="https://atrcheema.github.io/posts/2022/09/blog-post-1/"><![CDATA[<p>Scikit-learn has been the lingua-franca of machine learning community for over a decade now. Developed
in early 2000’s, the paper about scikit-learn came in 2011 and 2013. The paper is well written, and describes the
basic principles of the library. The very fact that the paper of a machine learning library is still very
much relevant today, speaks volumes about the strong principles and robust software design of scikit-learn
library. No doubt, the library has influenced many onward machine learning libraries and almost all the ‘mainstream’ machine
learning libraries have borrowed many concepts from it. Since the success of scikit-learn, there has been plethora
of scikits, sickit-optimize for optimization of hyperparameters, scikit-image for image processing, to name a few.
This post has been influenced by the original paper and my experience of using scikit-learn. The purpose is to provide an overview of scikit-learn with code examples.</p>

<p>estimators</p>

<p>predictors</p>

<p>transformers</p>

<p>model selection</p>

<p>pipelines</p>

<p>limitations</p>

<p>extending</p>]]></content><author><name>Ather Abbas</name></author><category term="scikit-learn" /><category term="ML tooling" /><category term="python libraries" /><summary type="html"><![CDATA[Scikit-learn has been the lingua-franca of machine learning community for over a decade now. Developed in early 2000’s, the paper about scikit-learn came in 2011 and 2013. The paper is well written, and describes the basic principles of the library. The very fact that the paper of a machine learning library is still very much relevant today, speaks volumes about the strong principles and robust software design of scikit-learn library. No doubt, the library has influenced many onward machine learning libraries and almost all the ‘mainstream’ machine learning libraries have borrowed many concepts from it. Since the success of scikit-learn, there has been plethora of scikits, sickit-optimize for optimization of hyperparameters, scikit-image for image processing, to name a few. This post has been influenced by the original paper and my experience of using scikit-learn. The purpose is to provide an overview of scikit-learn with code examples.]]></summary></entry></feed>