The correct answer is False. Dropout is a computationally inexpensive technique compared to bagging.
Dropout is a regularization technique that is used to prevent overfitting. It works by randomly dropping out (i.e., setting to zero) units during training. This forces the model to learn to rely on other units, which makes it less likely to overfit to the training data.
Bagging is a technique that is used to reduce variance in a model. It works by creating multiple models from the same training data and then averaging the predictions of the models. This reduces the impact of noise in the training data and makes the model more robust.
Dropout is computationally less expensive than bagging because it does not require creating multiple models. It is also less computationally expensive than other regularization techniques, such as L2 regularization.
Here is a table that summarizes the differences between dropout and bagging:
| Feature | Dropout | Bagging |
|—|—|—|
| Purpose | Regularization | Reduce variance |
| How it works | Randomly drops out units during training | Creates multiple models from the same training data and then averages the predictions of the models |
| Computational cost | Less computationally expensive | More computationally expensive |
I hope this helps! Let me know if you have any other questions.