Given these challenges, which combination of techniques is the MOST LIKELY to help prevent overfitting and improve the model’s performance on unseen data?

You are a data scientist working on a deep learning model to classify medical images for disease detection. The model initially shows high accuracy on the training data but performs poorly on the validation set, indicating signs of overfitting. The dataset is limited in size, and the model is complex, with many parameters. To improve generalization and reduce overfitting, you need to implement appropriate techniques while balancing model complexity and performance.

Given these challenges, which combination of techniques is the MOST LIKELY to help prevent overfitting and improve the model’s performance on unseen data?
A . Prune the model by removing less important layers and nodes, and use L2 regularization to reduce the magnitude of the model’s weights, preventing overfitting
B . Use ensembling by combining multiple versions of the same model trained with different random seeds, and apply data augmentation to artificially increase the size of the dataset
C . Combine data augmentation to increase the diversity of the training data with early stopping to prevent overfitting, and use ensembling to average predictions from multiple models
D . Apply early stopping to halt training when the validation loss stops improving, and use dropout as a
regularization technique to prevent the model from becoming too reliant on specific neurons

View Answer

Answer: C

Explanation:

Correct option:

Combine data augmentation to increase the diversity of the training data with early stopping to prevent overfitting, and use ensembling to average predictions from multiple models

via – https://aws.amazon.com/what-is/overfitting/

This option combines data augmentation to artificially expand the training dataset, which is crucial when

data is limited, with early stopping to prevent the model from overtraining. Additionally, ensembling helps improve generalization by averaging predictions from multiple models, reducing the likelihood that overfitting in any single model will dominate the final prediction. This combination addresses both data limitations and model overfitting effectively.

Incorrect options:

Apply early stopping to halt training when the validation loss stops improving, and use dropout as a regularization technique to prevent the model from becoming too reliant on specific neurons – Dropout is a form of regularization used in neural networks that reduces overfitting by trimming codependent neurons. Early stopping and dropout are effective techniques for preventing overfitting, particularly in deep learning models. However, while they can help, they may not be sufficient alone, especially when dealing with limited data. Combining these techniques with others, such as data augmentation or ensembling, would provide a more robust solution.

Use ensembling by combining multiple versions of the same model trained with different random seeds, and apply data augmentation to artificially increase the size of the dataset – Ensembling and data augmentation are powerful techniques, but ensembling by combining multiple versions of the same model trained with different random seeds might not provide significant diversity in predictions. A combination of diverse models or more comprehensive techniques might be more effective.

Prune the model by removing less important layers and nodes, and use L2 regularization to reduce the magnitude of the model’s weights, preventing overfitting – Regularization helps prevent linear models from overfitting training data examples by penalizing extreme weight values. L1 regularization reduces the number of features used in the model by pushing the weight of features that would otherwise have very small weights to zero. L1 regularization produces sparse models and reduces the amount of noise in the model. L2 regularization results in smaller overall weight values, which stabilizes the weights when there is high correlation between the features. Pruning and L2 regularization are useful for reducing model complexity and preventing overfitting. However, pruning can sometimes lead to underfitting if not done carefully, and using these techniques alone might not fully address the overfitting issue, especially with limited data.

References:

https://aws.amazon.com/what-is/overfitting/

https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec-hyperparameters.html

https://docs.aws.amazon.com/machine-learning/latest/dg/training-parameters.html