From 09c5ac6e69c961d337623bb44f3deee91c9d7f7f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9s=20Alejos?= <andres.alejos@gmail.com>
Date: Sat, 8 Jun 2024 05:14:48 -0400
Subject: [PATCH] Update EXGBoost version in notebook (#279)

---
 notebooks/cv_gradient_boosting_tree.livemd | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/notebooks/cv_gradient_boosting_tree.livemd b/notebooks/cv_gradient_boosting_tree.livemd
index 26e413eb..5e6c41ce 100644
--- a/notebooks/cv_gradient_boosting_tree.livemd
+++ b/notebooks/cv_gradient_boosting_tree.livemd
@@ -3,7 +3,7 @@
 ```elixir
 Mix.install([
   {:scholar, "~> 0.2.0"},
-  {:exgboost, "~> 0.3"},
+  {:exgboost, "~> 0.5"},
   {:req, "~> 0.3.9"},
   {:kino_vega_lite, "~> 0.1.9"},
   {:kino, "~> 0.10.0"},
@@ -22,6 +22,13 @@ require Explorer.DataFrame, as: DF
 require Explorer.Series, as: S
 ```
 
+And let's configure `EXLA` as our default backend (where our tensors are stored) and compiler (which compiles Scholar code) across the notebook and all branched sections:
+
+```elixir
+Nx.global_default_backend(EXLA.Backend)
+Nx.Defn.global_default_options(compiler: EXLA)
+```
+
 We are going to work with [Medical Cost Personal Datasets](https://www.kaggle.com/datasets/mirichoi0218/insurance) to predict medical charges that were applied to each person from the dataset. Let's download it:
 
 ```elixir