A Deep Dive into Powerful Scikit-Learn Functions for Machine Learning
Introduction
Machine learning has come a long way in recent years, with the development of new tools and techniques enabling us to build more powerful and accurate models.
One such tool is Scikit-Learn, a popular Python library that provides a range of functions for building and training machine learning models. While many users are familiar with the basic functions offered by Scikit-Learn, there are a number of advanced features that can help take your machine learning to the next level.
In this blog, we will explore some of these advanced functions and how they can be used to improve the accuracy and effectiveness of your models.
1. sklearn.isotonic.IsotonicRegression
Isotonic regression is a non-parametric regression technique that fits a piecewise-constant function to the data. It is useful when the relationship between the predictor and response variables is not monotonic or when the data contains outliers. The sklearn.isotonic.IsotonicRegression class provides an implementation of isotonic regression.
Here’s an example of how to use IsotonicRegression on a toy dataset:
