Gradient Boosting is a popular boosting algorithm. In gradient boosting, each predictor corrects its predecessor’s error. In contrast to Adaboost, the weights of the training instances are not tweaked, instead, each predictor is trained using the residual errors of predecessor as labels.
There is a technique called the Gradient Boosted Trees whose base learner is CART (Classification and Regression Trees).
The below diagram explains how gradient boosted trees are trained for regression problems.
The ensemble consists of N trees. Tree1 is trained using the feature matrix X and the labels y. The predictions labelled y1(hat) are used to determine the training set residual errors r1. Tree2 is then trained using the feature matrix X and the residual errors r1 of Tree1 as labels. The predicted results r1(hat) are then used to determine the residual r2. The process is repeated until all the N trees forming the ensemble are trained.
There is an important parameter used in this technique known as Shrinkage.
Each tree predicts a label and final prediction is given by the formula,
y(pred) = y1 + (eta * r1) + (eta * r2) + ....... + (eta * rN)
The class of the gradient boosting regression in scikit-learn is GradientBoostingRegressor. A similar algorithm is used for classification known as GradientBoostingClassifier.
Code: Python code for Gradient Boosting Regressor
RMSE test set: 4.01
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.
- Boosting in Machine Learning | Boosting and AdaBoost
- ML | XGBoost (eXtreme Gradient Boosting)
- LightGBM (Light Gradient Boosting Machine)
- Difference between Batch Gradient Descent and Stochastic Gradient Descent
- Comparison b/w Bagging and Boosting | Data Mining
- ML | Stochastic Gradient Descent (SGD)
- Optimization techniques for Gradient Descent
- Gradient Descent in Linear Regression
- Python | Plotting an Excel chart with Gradient fills using XlsxWriter module
- ML | Mini-Batch Gradient Descent with Python
- Gradient Descent algorithm and its variants
- Python | Morphological Operations in Image Processing (Gradient) | Set-3
- ML | Momentum-based Gradient Optimizer introduction
- PyQt5 - Gradient color Bar of Progress Bar
- PyQt5 - Gradient Color Progress Bar
- How to find Gradient of a Function using Python?
- Multivariate Optimization - Gradient and Hessian
- Difference between Gradient descent and Normal equation
- Python - tensorflow.GradientTape.gradient()
- Make a gradient color mapping on a specified column in Pandas
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.