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 two main components of a dataset in supervised machine learning?

• Data points and observations

• Classes and targets

• Inputs and outputs

• Labels and features

In the context of machine learning, what is a label?

• A type of feature

• The input data

• A type of dataset

• The output or target of data

Which of the following terms is NOT interchangeable with 'label'?

• Target

• Output

• Class

• Feature

Why do we convert non-numeric labels into numeric form?

• Because computers only understand numeric data

• To reduce data size

• To make them more readable

• To improve data security

What is a feature in the context of machine learning?

• A type of dataset

• An individual measurable property

• A type of label

• A machine learning model

How are features related to observations in machine learning?

• Features are unrelated to observations

• Features are the measurable properties observed

• Features are the inputs used to make observations

• Features are the results of observations

What is the importance of selecting the right features in a model?

• It reduces the model's complexity

• It affects the model's accuracy

• It increases the model's size

• It determines the model's speed

How does a computer make predictions based on features and labels?

• By memorizing all data

• By using pre-defined rules

• By randomly guessing

• By using learned patterns from data

What happens when a computer encounters a new combination of features?

• It asks for more data

• It makes a random guess

• It ignores the data

• It uses learned patterns to predict

What is the next topic after understanding datasets, labels, and features?

• Algorithm selection

• Feature engineering

• Data splitting

• Model evaluation