From f221e7cd6f6706668c628c9bea0cf83c33389680 Mon Sep 17 00:00:00 2001
From: dts <speckhard@fhi.mpg.de>
Date: Tue, 23 May 2023 21:05:24 +0200
Subject: [PATCH] Add a conclusion.
---
cbs_with_qrf.ipynb | 10 ++++++++++
1 file changed, 10 insertions(+)
diff --git a/cbs_with_qrf.ipynb b/cbs_with_qrf.ipynb
index 079b5bf..a6d415e 100644
--- a/cbs_with_qrf.ipynb
+++ b/cbs_with_qrf.ipynb
@@ -2685,6 +2685,16 @@
"plt.tight_layout()\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eba4149d",
+ "metadata": {},
+ "source": [
+ "# Conclusion\n",
+ "\n",
+ "In this tutorial you've learned how machine learning methods can be used to perform CBS extrapolation of total energies. Specifically, we used the quantile random forest algorithm to perform inferences and the get prediction intervals. We analyzed the method's effective with respect to other models in the literature and also looked at how the model is built from the features and how it performs across the wide r"
+ ]
}
],
"metadata": {
--
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