Data Science 🐍 Features

The video tutorial introduces key machine learning terminology, focusing on concepts like features, labels, and loss functions. It explains feature generation and selection using stock data, highlighting methods like volatility analysis and feature importance. The tutorial also includes a TCC Lab activity, demonstrating feature development to predict heater status using temperature and derivatives.

What are the inputs to a model in data science referred to as?

Explain the concept of a loss function in machine learning.

How do weights in a model affect the loss function?

What is the difference between supervised and unsupervised learning?

What role do features play in predicting outcomes in machine learning?

What are the potential risks of using simplified models for stock predictions?

Describe the process of feature selection and its importance.

What is the significance of the chi-squared statistical test in feature selection?

How can correlation between features impact model performance?

Explain how temperature and its derivatives can be used to predict heater status.