ElixirConf 2023 - Andrés Alejos - Nx Powered Decision Trees

Discover how to leverage the power of decision trees for classification and regression tasks with Nx and XGBoost at ElixirConf 2023.

Key takeaways
  • Decision trees are a type of machine learning model that can be used for classification and regression tasks.
  • They work by recursively splitting the data into smaller and smaller subsets based on the values of the features.
  • Each split is made by selecting the feature that best separates the data into two groups.
  • The process continues until a stopping criterion is met, such as a maximum depth or a minimum number of samples in a leaf node.
  • Decision trees are relatively easy to understand and interpret, which makes them a good choice for many applications.
  • However, they can be prone to overfitting, which can be mitigated by using techniques such as pruning and bagging.
  • XGBoost is a popular library for training and using decision trees.
  • It provides a number of features that make it easy to use, such as automatic feature selection and hyperparameter tuning.
  • Nx is a library for serving machine learning models in Elixir.
  • It can be used to serve decision trees that have been trained using XGBoost or other libraries.
  • Nx provides a number of features that make it easy to deploy and manage machine learning models, such as load balancing and automatic scaling.