XGBoost ❌ LightGBM ❌ CatBoost ❌ Scikit-Learn GRADIENT BOOSTING Performance Compared

In this video I'll compare the speed and accuracy of several gradient boosting implementations from Scikit-Learn, XGBoost, LightGBM and CatBoost.

There are so many available and many times you don't know which one to choose for your machine learning problem therefore in this video I'll train classifiers with each and then compare the speed and accuracy to see which one is the winner.

Of course we cannot generalize this on any type of problem and any dataset but the results are going to be interesting nevertheless.

We know that Gradient boosting is an ensemble algorithm that fits boosted decision trees by minimizing an error gradient.

So it fits a series of models and fits each successive model in order to minimize the error of the previous models.

It's a very effective machine learning algorithm and for a long time it has been one of the main algorithms used to win Kaggle machine learning competitions.

There are many implementations of the gradient boosting algorithm available in Python. Perhaps the most used implementation is the version provided by the XGBoost library, and I talked about that in a previous video here:
https://www.youtube.com/watch?v=4rikgkt4IcU

Now of course Scikit-Learn has an implementation as well and we have other libraries like LightGBM and CatBoost that offer their own implementations.

Depending on your project it's best to test each implementation of this algorithm so you get the best possible results.

But in this video I'm going to do a fun test to rank them based on mean accuracy and speed.

You can access the Jupyter notebook here (login required):
https://www.decisionforest.com/downloads/41

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Видео XGBoost ❌ LightGBM ❌ CatBoost ❌ Scikit-Learn GRADIENT BOOSTING Performance Compared канала DecisionForest