This content originally appeared on DEV Community and was authored by likhitha manikonda
What is a Decision Tree?
A decision tree is like a flowchart: it asks a series of questions about your data, and each answer leads to another question or a final prediction.
It splits the data into branches based on feature values, ending in “leaves” that represent predictions.
Where is it used?
Classification: Predicting categories (e.g., will a customer buy or not buy).
Regression: Predicting numbers (e.g., house price).
How Does a Decision Tree Work?
At each step, it chooses the feature and value that best splits the data to make predictions more accurate.
It keeps splitting until it reaches a stopping point (like a minimum number of samples or maximum depth).
Common Mistakes to Avoid
Overfitting: Tree is too deep and memorizes the training data. Use max_depth to limit tree size.
Ignoring Data Quality: Clean your data before training.
Not Evaluating: Always check accuracy or error on test data.
Continuation: https://dev.to/codeneuron/how-to-check-if-decision-trees-works-for-your-dataset-3232
This content originally appeared on DEV Community and was authored by likhitha manikonda