From 832e17cd2bca21f7fe40adf5cdf8b97204911734 Mon Sep 17 00:00:00 2001
From: JERRYenSHU503 <1929891932@qq.com>
Date: Sun, 28 Apr 2024 13:37:44 +0800
Subject: [PATCH] Update model-selection.ipynb

---
 .../ml-advanced/model-selection.ipynb                         | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb b/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb
index cef35b22f..4ce929857 100644
--- a/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb
+++ b/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb
@@ -498,7 +498,9 @@
    "source": [
     "First of all, let's take a look at a plot, this is a simple learning curve using an iris dataset in sklearn.dataset. We can simply notice the two curve we plot fells far apart when we have less examples, and when we enlarge the training examples we can see the two lines are approaching convergence.\n",
     "\n",
-    "This is how we can see the fitting process using learning curve."
+    "Why? \n",
+    "\n",
+    "To train a model, it is necessary to have a sufficient number of samples so that it can generalize patterns from the data. Assuming we have a function y=f(x), essentially, machine learning algorithms summarize and fit the f function based on a large number of (x, y) pairs. Therefore, if you have too few (x, y) pairs, the algorithm will not be able to summarize the function effectively. This is the impact of the sample size on the degree of fitting."
    ]
   },
   {