📝 Exercise M1.03¶
The goal of this exercise is to compare the statistical 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 statistical performance of these
baseline models.
import pandas as pd
adult_census = pd.read_csv("../datasets/adult-census.csv")
We will 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 dataset into a train and test sets.
from sklearn.model_selection import train_test_split
# Write your code here.
Use a DummyClassifier such that the resulting classifier will 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 refer to the parameter strategy of the DummyClassifier
to achieve the desired behaviour.
from sklearn.dummy import DummyClassifier
# Write your code here.