📝 Exercise M1.03

The goal of this exercise is to compare the performance of our classifier in the previous notebook (roughly 81% accuracy with LogisticRegression) to some simple baseline classifiers. The simplest baseline classifier is one that always predicts the same class, irrespective of the input data.

• 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 generalization performance of these baseline models.

import pandas as pd

adult_census = pd.read_csv("../datasets/adult-census.csv")

We first split our dataset to have the target separated from the data used to train our predictive model.

target_name = "class"
target = adult_census[target_name]
data = adult_census.drop(columns=target_name)

We start by selecting only the numerical columns as seen in the previous notebook.

numerical_columns = ["age", "capital-gain", "capital-loss", "hours-per-week"]

data_numeric = data[numerical_columns]

Split the data and target into a train and test set.

from sklearn.model_selection import train_test_split

# Write your code here.

Use a DummyClassifier such that the resulting classifier always predict the class ' >50K'. What is the accuracy score on the test set? Repeat the experiment by always predicting the class ' <=50K'.

Hint: you can set the strategy parameter of the DummyClassifier to achieve the desired behavior.

from sklearn.dummy import DummyClassifier

# Write your code here.