Hi muna,
I understand that you are concerned that your deep learning models are performing poor in camparison to machine learning models and you are wondering where did you went wrong. As the name suggests, deep learning networks such as CNNs have a large number of layers that might result in an overfitted DL model which gives poor accuracy on test dataset. You can try the following:
- Check if your training set is large enough. Deep learning models often require a large amount of data to generalize well. If your dataset is small, consider using techniques like data augmentation or transfer learning to overcome this limitation. You can also consider using transfer learning. This technique allows you to leverage pre-trained models on larger datasets and fine-tune them for your specific problem. This can often lead to better performance, even with limited data.
- Apply regularization techniques to prevent overfitting. Common techniques include dropout, weight decay (L2 regularization), and early stopping. Regularization helps the model generalize better to unseen data. You can also try using ensemble methods, such as model averaging or stacking, to combine the predictions of multiple models. This can help improve the overall accuracy by leveraging the strengths of different classifiers.
In case of potential errors or bugs, ensure that you are correctly loading and preprocessing your data, setting the correct labels, and splitting the dataset into training and testing sets.
Hope this helps!
