Update app.py

app.py

@@ -71,6 +71,8 @@ def create_plot(x1, y1, x2, y2, cov1, cov2, n1, n2, max_depth, n_estimators):
     return fig
 
 info = '''
+# AdaBoost Classifier Example on Gaussian Quantile Generated Data
+
 This example fits an [AdaBoost classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier) on two non-linearly separable classes. The samples are generated using two [Gaussian quantiles](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_gaussian_quantiles.html#sklearn.datasets.make_gaussian_quantiles) of configurable mean and covariance (see the sliders below).
 
 For the first generated Gaussian, the inner half quantile is assigned to Class A and the outer half quantile is assigned to class B. For the second generated quantile, the opposite assignment happens (inner = Class B, outer = Class A).