# Gradient Boosting Classification explained through Python

In this article, I would like to discuss how Gradient Boosting works for Classification. If you did not read that article, it’s all right because I will reiterate what I discussed in the previous article anyway.

In my previous article, I discussed and went through a working python example of Gradient Boosting for Regression. In this article, I would like to discuss how Gradient Boosting works for Classification. If you did not read that article, it’s all right because I will reiterate what I discussed in the previous article anyway. So, let’s get started!

## Ensemble Methods

Usually, you may have a few good predictors, and you would like to use them all, instead of painfully choosing one because it has a 0.0001 accuracy increase. In comes _Ensemble Learning. _In Ensemble Learning, instead of using a single predictor, multiple predictors and training in the data and their results are aggregated, usually giving a better score than using a single model. A Random Forest, for instance, is simply an ensemble of bagged(or pasted) Decision Trees.

You can think of Ensemble Methods as an orchestra; instead of just having one person play an instrument, multiple people play different instruments, and by combining all the musical groups, the music sound generally better than it would if it was played by a single person.

_While Gradient Boosting is an Ensemble Learning method, it is more specifically a _Boosting Technique. So, what’s Boosting?

## Boosting

Boosting _is a special type of Ensemble Learning technique that works by combining several _weak learners(_predictors with poor accuracy) _into a strong learner(a model with strong accuracy). This works by each model paying attention to its predecessor’s mistakes.

The two most popular boosting methods are:

We will be discussing Gradient Boosting.

## AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

## Beginners Guide to Machine Learning on GCP

This blog covers basic knowledge needed to get started ML journey on GCP. Machine Learning is a way to use some set of algorithms to derive predictive analytics from data.

## 3 Ways to Select Features Using Machine Learning Algorithms in Python

3 Ways to Select Features Using Machine Learning Algorithms in Python. In this article, take a look at three ways to select features using machine learning learning algorithms in Python.

## A Beginner’s Guide to Supervised Machine Learning Algorithms

An overview of supervised machine learning algorithms that are commonly used. Supervised learning algorithms try to predict a target (dependent variable) using features (independent variables). Depending on the characteristics of target variable, it can be a classification (discrete target variable) or a regression (continuous target variable) task. The algorithms we will cover: Linear Regression, Support Vector Machines, Naive Bayes, Logistics Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosted Decision Trees,

## Learning in Artificial Intelligence - Great Learning

What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?