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Individual-and-Voting-Regression-Predictions

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merve HF Staff commited on May 1, 2023

Commit

8294611

1 Parent(s): 7dae04b

Update app.py

Files changed (1) hide show

app.py CHANGED Viewed

@@ -71,12 +71,12 @@ def app_fn(n: int) -> go.Figure:
     return fig
-title="Individual and Voting (🗳️) Regression Predictions"
 with gr.Blocks() as demo:
     gr.Markdown(f"# {title}")
     gr.Markdown(
         """
-        ## A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. \
         Then it averages the individual predictions to form a final prediction. This example will use three different regressors to \
         predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression. Then the 3 regressors will be used for the VotingRegressor. \
         The dataset used consists of 10 features collected from a cohort of diabetes patients. The target is a quantitative measure of disease progression one year after baseline.

     return fig
+title="Individual and Voting Regression Predictions 🗳️"
 with gr.Blocks() as demo:
     gr.Markdown(f"# {title}")
     gr.Markdown(
         """
+        A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. \
         Then it averages the individual predictions to form a final prediction. This example will use three different regressors to \
         predict the data: GradientBoostingRegressor, RandomForestRegressor, and LinearRegression. Then the 3 regressors will be used for the VotingRegressor. \
         The dataset used consists of 10 features collected from a cohort of diabetes patients. The target is a quantitative measure of disease progression one year after baseline.