For categorical features in NNs

1. Numerical Representation:

   • NNs operate on numerical data
   • Categorical features need to be converted into a numerical format for the neural network to process.

2. Preventing Biased Learning:

   • Assigning integer labels to categories may introduce biases during training. The network could interpret the numerical assignments as meaningful/ordinal, impacting the model's performance
   • One-hot encoding eliminates this potential bias.

3. Compatibility with Activation Functions:

   • Neural networks use activation functions that assume numeric inputs.
   • One-hot encoding facilitates compatibility with these functions, ensuring that the network can effectively learn complex patterns from categorical data.

4. Handling Varying Numbers of Categories:

   • Categorical features with different numbers of categories can be uniformly represented using one-hot encoding.
   • crucial for the neural network to process inputs correctly.