We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
ElixirConf 2023 - Andrés Alejos - Nx Powered Decision Trees
Explore the power of Nx powered decision trees in machine learning, covering classification, regression, feature selection, and more, as presented at ElixirConf 2023 by Andrés Alejos.
- Decision trees can be used for classification problems where the target is a class.
- Decision trees can be grown to a specified depth or limited by a stopping criterion.
- The optimal splits of a decision tree can be determined using various criteria such as Gini impurity or information gain.
- Decision trees can be trained using a variety of algorithms such as gradient boosting.
- XGBoost is a popular and efficient algorithm for training decision trees.
- Decision trees can be used for feature selection and dimensional reduction.
- Decision trees can be used for regression problems where the target is a continuous value.
- Decision trees can be used for clustering problems where the target is a cluster label.
- Decision trees can be used for data preprocessing and feature engineering.
- Decision trees can be used for model interpretability and variable importance.
- Decision trees can be used for model ensembling and stacking.
- Decision trees can be used for time series forecasting and prediction.
- Decision trees can be used for recommender systems and content-based filtering.
- Decision trees can be used for information retrieval and text classification.
- Decision trees can be used for natural language processing and speech recognition.