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  # %% [markdown]
  # #  Exercise 01
  #
  # The goal of is to compare the performance of our classifier (81% accuracy) to some baseline classifiers that  would ignore the input data and instead make constant predictions.
  #
  # The online [documentation for DummyClassifier](https://scikit-learn.org/stable/modules/model_evaluation.html#dummy-estimators) gives instructions on how to use it.
  
  # %%
  import pandas as pd
  
  df = pd.read_csv(
      "https://www.openml.org/data/get_csv/1595261/adult-census.csv")
  
  # %%
  target_name = "class"
  target = df[target_name].to_numpy()
  data = df.drop(columns=[target_name, "fnlwgt"])
  numerical_columns = [
      c for c in data.columns if data[c].dtype.kind in ["i", "f"]]
  data_numeric = data[numerical_columns]
  
  # %%
  from sklearn.model_selection import cross_val_score
  from sklearn.dummy import DummyClassifier
  
  # TODO: write me!
  