Answer: Overfitting is avoided by carrying out calculations for the validation data set at the same time as the training data set.

## Explanation Let's analyze each statement: **Statement A: True** - A neural network without activation functions becomes a linear transformation, equivalent to linear regression. **Statement B: True** - A neural network with no hidden layers (just input to output) is indeed equivalent to linear regression. **Statement C: True** - The bias term in neural networks serves the same purpose as the constant/intercept term in regression models. **Statement D: False** - This statement is incorrect because: - Overfitting is NOT avoided by calculating validation data simultaneously with training data - In fact, calculating validation data during training is standard practice to monitor overfitting - Overfitting is typically avoided through techniques like: - Early stopping (monitoring validation loss) - Regularization (L1/L2) - Dropout - Cross-validation - Proper train/validation/test split Therefore, statement D is the false statement as it misrepresents how overfitting is prevented in neural networks.

Author: LeetQuiz .