Finding class imbalance
You are focusing on the important preliminary phase of the machine learning lifecycle: Exploratory Data Analysis (EDA).
EDA allows you to better understand the nature of the heart_disease_df
dataset, including the relationships between different variables, and potential problems that might need to be addressed before you move on to training your model. Understanding the distribution of classes in your features - for example, patient sex - is a key part of EDA.
Class imbalance, where one class has significantly more samples than another, can potentially bias your model's training process, leading it to favor the majority class.
This exercise is part of the course
End-to-End Machine Learning
Exercise instructions
- Print out the class balance of the
sex
column.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print the sex value counts of the heart disease dataset
print(____[____].____)