Machine Learning Random Forest with Python from Scratch - Labels and Features

The video tutorial introduces the concepts of data sets, labels, and features in machine learning. It explains the role of labels as outputs and features as observations, highlighting their importance in supervised learning. The tutorial also discusses how features and labels are used together to train models and make predictions.

What are the key terminologies associated with supervised machine learning?

Explain the difference between supervised and unsupervised machine learning.

What role do labels play in a dataset?

What does it mean when we say that labels are interchangeable with targets?

What is the significance of converting labels into numeric form?

How can features be defined in the context of machine learning?

What is the relationship between observations and features in a dataset?

What are some common feature extraction techniques used in machine learning?

How does the performance of a model depend on the features selected?

Describe how a model is trained in machine learning.