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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.
- 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.