Update app.py

app.py

@@ -53,6 +53,8 @@ def create_plot(outlier_ratio=0.1, epsilon=1.35):
 
 
 info = '''
+# Robustness Against Outliers: Huber vs Ridge Regression
+
 This example demonstrates a simple linear regression problem in the existence of outliers, and compares the effectiveness of Huber regression vs Ridge regression.
 
 [Ridge regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html), which is essentially basic L2 linear regression with regularization (but regularization is neglected here), suffers from outliers because the outlying data points are going to heavily increase the loss, forcing the best-fit line to lean towards the outliers to decrease that loss.