Scikit-learn is the most useful library for machine learning in Python. The Scikit library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering.
Components of scikit-learn:
Scikit-learn comes loaded with a lot of features. Here are a few of them to help you understand the spread:
- Supervised learning algorithms: Think of any supervised machine learning algorithm you might have heard about and there is a very high chance that it is part of scikit-learn. Starting from Generalized linear models (e.g Linear Regression), Support Vector Machines (SVM), Decision Trees to Bayesian methods – all of them are part of scikit-learn toolbox. The spread of machine learning algorithms is one of the big reasons for the high usage of scikit-learn. I started using scikit to solve supervised learning problems and would recommend that to people new to scikit / machine learning as well.
- Cross-validation: There are various methods to check the accuracy of supervised models on unseen data using sklearn.
- Unsupervised learning algorithms: Again there is a large spread of machine learning algorithms in the offering – starting from clustering, factor analysis, principal component analysis to unsupervised neural networks.
- Various toy datasets: This came in handy while learning scikit-learn. I had learned SAS using various academic datasets (e.g. IRIS dataset, Boston House prices dataset). Having them handy while learning a new library helped a lot.
- Feature extraction: Scikit-learn for extracting features from images and text (e.g. Bag of words)
Library URL: https://scikit-learn.org/stable/