Chapter 10 – Best Practices for Machine Learning and Feature Engineering

1. What is the difference between feature engineering and feature selection?

Feature selection is a part of feature engineering.

2. What is the difference between principal component analysis (PCA) and feature selection?

Feature selection takes the dataset and gives us the best set of features, while PCA is a dimensionality reduction method.

3. How can we encode features like dates and hours?

One of the techniques is adding the (sine, cosine) transformation of the time of day variable.

4. Why it is useful to print out training and testing accuracy?

It is useful to detect overfitting by comparing the two metrics.

5. How can we deploy a machine learning model and use it in a product?

There are many ways to take a machine learning model to production, such as web services and containerization depending on...