One Hot Encoding in Machine Learning

Introduction

• Objective: Understand one-hot encoding and its usage in machine learning.
• Previously discussed: Labels for images encoded as one-hot vectors.

Supervised Learning and Labels

• During training, labeled inputs are passed to the model resulting in predicted outputs.
• Example: Image classifier with labeled images of animals.
• Labels & outputs typically not words (e.g., 'cat', 'dog') but encoded into integers or vectors.

What is One Hot Encoding?

• Definition: Transforms categorical labels into vectors of zeros and ones.
• Vector Length: Equal to the number of classes/categories.
  • Example: Two categories (cat, dog) = vector length of 2.
  • Three categories (cat, dog, lizard) = vector length of 3.

Detailed Explanation

• Index Association: Each element in the vector corresponds to a category.
  • E.g., Cat -> 1st element, Dog -> 2nd element, Lizard -> 3rd element.
• Vector Composition: All elements are 0 except the one corresponding to the actual category.
• Examples:
  • Cat: [1, 0, 0]
  • Dog: [0, 1, 0]
  • Lizard: [0, 0, 1]

Adding More Categories

• Example with added category (llama):
  • Four categories: Cat, Dog, Lizard, Llama -> vector length of 4.
  • Encoding:
    • Cat: [1, 0, 0, 0]
    • Dog: [0, 1, 0, 0]
    • Lizard: [0, 0, 1, 0]
    • Llama: [0, 0, 0, 1]
• Order Flexibility: The order can vary based on the underlying code or library.
  • Example variation:
    • Dog: [1, 0, 0, 0]
    • Lizard: [0, 1, 0, 0]
    • Llama: [0, 0, 1, 0]
    • Cat: [0, 0, 0, 1]

Practical Tips

• Check how encoding is mapped in the chosen library (e.g., Keras for image data).
• Refer to previous tutorials for detailed steps on accessing this mapping.

Conclusion

• One-hot encoding is crucial for transforming labels for classification tasks in neural networks.
• Ensures categorical data is in a format suitable for model processing.