Feature Distribution and Target Analysis
Analyzing feature-target relationships, class imbalance detection, mutual information.
Why Analyze Features Against the Target?
The target is the variable you want to predict. The point of EDA for modeling is to learn which features actually relate to that target.
This lesson covers feature-vs-target plots, measuring informativeness with mutual information, detecting class imbalance, and fixing skew with transforms.
Feature vs Target — Numeric Target
For a numeric target, a scatter plot of feature vs target shows whether the feature carries signal.
A clear trend means the feature is predictive; a shapeless cloud means it probably is not.
import seaborn as sns
import matplotlib.pyplot as plt
sns.scatterplot(x="rooms", y="price", data=df, alpha=0.4)
plt.show()All lessons in this course
- EDA Workflow and Data Profiling
- Univariate Analysis
- Bivariate and Multivariate Analysis
- Feature Distribution and Target Analysis