Exercise 01ΒΆ
The goal of this exercise 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.
What would be the score of a model that always predicts
' >50K'?What would be the score of a model that always predicts
' <= 50K'?Is 81% or 82% accuracy a good score for this problem?
Use a DummyClassifier and do a train-test split to evaluate
its accuracy on the test set. This
link
shows a few examples of how to evaluate the performance of these baseline
models.
import pandas as pd
df = pd.read_csv("../datasets/adult-census.csv")
target_name = "class"
target = df[target_name].to_numpy()
data = df.drop(columns=[target_name, "fnlwgt"])
from sklearn.compose import make_column_selector as selector
numerical_columns_selector = selector(dtype_include=["int", "float"])
numerical_columns = numerical_columns_selector(data)
data_numeric = data[numerical_columns]
from sklearn.model_selection import train_test_split
from sklearn.dummy import DummyClassifier
# TODO: write me!