How does encoding affect performance in machine learning models?

How does encoding affect performance in machine learning models?

Encoding is crucial as it transforms categorical data into numerical format, which models can process. Different encoding techniques can significantly impact model accuracy and training speed.

What are some common encoding techniques?

Common techniques include One-Hot Encoding, Label Encoding, and Binary Encoding. Each has its pros and cons depending on the dataset.

Which encoding method is the best for decision trees?

For decision trees, Label Encoding is often preferred because it maintains the ordinal relationship of categories, which can be beneficial.

Can encoding lead to overfitting?

Yes, improper encoding can lead to overfitting, especially if the model learns noise from high cardinality features. It's essential to choose the right method.

How can I evaluate the impact of encoding on my model?

You can evaluate the impact by comparing model performance metrics like accuracy, precision, and recall across different encoding methods.