Deep Learning - Deep Neural Network for Beginners Using Python - One-Hot Encoding

The video tutorial explains how to convert model scores into probability distributions to assess prediction accuracy. It introduces the concept of one hot encoding, a method to convert categorical data into a numerical format that machines can understand. The tutorial addresses challenges with encoding when dealing with numerous classes and provides a detailed explanation of implementing one hot encoding by creating separate columns for each class, ensuring that non-numeric features are converted into a numeric format.

What is the purpose of converting model scores into a probability distribution?

• To increase the model's accuracy

• To reduce the complexity of the model

• To improve data visualization

• To understand the certainty of predictions

Why might using simple numeric labels for multiple classes be problematic?

• It requires more computational power

• It creates large differences in probability functions

• It can lead to biased predictions

• It simplifies the model too much

What is one-hot encoding primarily used for?

• To enhance data security

• To convert non-numeric features into numeric format

• To compress data for storage

• To improve the speed of data processing

In one-hot encoding, how is a class represented?

• By a unique numeric value

• By a single binary digit

• By a separate column with binary values

• By a combination of letters and numbers

What happens to the values in the columns of one-hot encoding when a class is present?

• The value is set to 2

• The value is set to 1

• The value is set to 0

• The value is set to 3