Comparison between TPOT and Auto-sklearn, two AutoML libraries. TPOT uses genetic algorithms, is fast and user-friendly For the incipient. Auto-sklearn, based on the meta-learning, offers a advanced customization. Choose TPOT for simplicity, Auto-sklearn for flexibility and deep customization based on specific needs.
AutoML is a machine learning method that automates the entire machine learning process, including feature engineering, model selection, and hyperparameter optimization. This allows developers to create machine learning models in record time, and to focus as much as possible on tasks with high added value. In this article, we are going to compare two popular AutoML libraries: TPOT and Auto-sklearn.
TPOT (Tree-based Pipeline Optimization Tool) is an open source AutoML library. TPOT uses genetic algorithms to optimize machine learning modeling pipelines. A genetic algorithm is an optimization technique inspired by natural selection, several generations are created and only the best individuals are retained.
TPOT supports a wide variety of machine learning models, such as decision trees, neural networks, random forests, and SVMs. Once the best model has been trained, it is possible to export the python code to create and train the ML model.
Because of the use of genetic algorithms, the results may be different each time the model is trained.
Auto-sklearn is another open source AutoML library. This library uses Bayesian optimization to select and optimize machine learning models. Bayesian optimization is a strategy seeking the extrema of an objective function, it is a method used when the objective function is very expensive to calculate.
A second method used by this library is meta-learning. This method consists in anticipating the performance of a model on certain data. It makes it possible to avoid testing models considered useless and to optimize calculation time.
Like TPOT, it also supports a wide variety of machine learning models like decision trees, neural networks, random forests, and SVMs.
As seen above, TPOT is based on genetic algorithms, while auto-sklearn uses meta-learning. In terms of performance, both libraries have comparable results, although TPOT is generally faster than auto-sklearn. When it comes to ease of use, TPOT is simpler to use than auto-sklearn because it doesn't require programming knowledge.
In summary, TPOT and auto-sklearn are two great AutoML libraries. TPOT stands out for its ease of use and speed, while auto-sklearn offers greater flexibility and better customization due to the fact that the library is built on the basis of Sklearn. Auto-sklearn allows greater freedom in the choice of algorithms where TPOT will be more focused on tree algorithms (Random Forest, Decision Tree...)
The choice between the two will depend on your specific needs. For AutoML beginners, TPOT is highly recommended. However, if you are looking for greater customization and greater flexibility, auto-sklearn can be a great option
