Update description and remove max_depth adjustment

app.py

@@ -14,7 +14,7 @@ def train_estimators(max_depth,n_estimators):
 
     regr_1 = DecisionTreeRegressor(max_depth=4)
     regr_2 = AdaBoostRegressor(
-        DecisionTreeRegressor(max_depth=max_depth), n_estimators=n_estimators, random_state=rng
+        DecisionTreeRegressor(max_depth=4), n_estimators=n_estimators, random_state=rng
     )
     regr_1.fit(X, y)
     regr_2.fit(X, y)
@@ -24,8 +24,8 @@ def train_estimators(max_depth,n_estimators):
 
     fig, ax = plt.subplots()
     ax.scatter(X, y, color=colors[0], label="training samples")
-    ax.plot(X, y_1, color=colors[1], label="Decision tree (max_depth=4)", linewidth=2)
-    ax.plot(X, y_2, color=colors[2], label=f"Adaboost (max_depth={max_depth}, estimators={n_estimators})", linewidth=2)
+    ax.plot(X, y_1, color=colors[1], label=f"Decision tree (estimators=1)", linewidth=2)
+    ax.plot(X, y_2, color=colors[2], label=f"Adaboost (estimators={n_estimators})", linewidth=2)
     ax.set_xlabel("data")
     ax.set_ylabel("target")
     ax.legend()
@@ -34,14 +34,18 @@ def train_estimators(max_depth,n_estimators):
 title = "Decision Tree Regression with AdaBoost"
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
-    gr.Markdown("This app demonstrates boosting of decision tree regressor using Adaboost. This app is developed based on [scikit-learn example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py)")
+    gr.Markdown("""
+    This app demonstrates boosting of decision tree regressor using Adaboost. Boosting algorithms work by combining multiple models (weak learners) to reach the final output (strong learners).
+    A single decision tree trained on randomly generated regression dataset is used as baseline and compared with a boosted decision tree trained on the same dataset. 
+    The outputs of each model are visualize together with actual data in the plot
+    The number of estimator used in boosted decision tree can be adjusted and the effect of this adjusment can be seen in the resulting plot.
+    This app is developed based on [scikit-learn example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py)
+    """)
 
-    max_depth = gr.Slider(minimum=1, maximum=50, step=1, label = "Maximum Depth")
-    n_estimators = gr.Slider(minimum=1, maximum=300, step=1, label = "Number of Estimators")
+    n_estimators = gr.Slider(minimum=2, maximum=300, step=1, label = "Number of Estimators")
 
     plot = gr.Plot(label=title)
-    n_estimators.change(fn=train_estimators, inputs=[max_depth,n_estimators], outputs=[plot])
-    max_depth.change(fn=train_estimators, inputs=[max_depth,n_estimators], outputs=[plot])
+    n_estimators.change(fn=train_estimators, inputs=n_estimators, outputs=[plot])
 
 demo.launch()