Update app.py

app.py

@@ -23,7 +23,7 @@ This method is similar to cross-validation, but it does not require repeated mod
 The **OOB** estimator provides a conservative estimate of the true test loss but is still a reasonable approximation for a small number of trees. 
 In this demonstration, a **GradientBoostingClassifier** model is trained on a simulation dataset, and the loss of the training set, test set, and OOB set are displayed in the figure.
 This information allows you to determine the level of generalization of your trained model on the simulation dataset.
-You can play around with ``number of samples``,``number of splits fold``, ``random seed``and ``number of estimator (step)``
+You can play around with ``number of samples``,``number of splits fold``, ``random seed``and ``number of estimation step``
 
 ## Dataset
 
@@ -147,7 +147,7 @@ with gr.Blocks(theme=theme) as demo:
     n_samples = gr.Slider(minimum=500, maximum=5000, step=500, value=500, label="Number of samples")
     n_splits = gr.Slider(minimum=2, maximum=10, step=1, value=3, label="Number of cross validation folds")
     random_seed = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random seed")
-    n_estimators = gr.Slider(minimum=500, maximum=2000, step=100, value=500, label="Number of estimator step")
+    n_estimators = gr.Slider(minimum=500, maximum=2000, step=100, value=500, label="Number of step")
 
     with gr.Row():
         with gr.Column():