I have a dataset from lung cancer images with 163 samples (2D images). I use the fine-tuning of deep learning algorithms to classify samples, but the validation loss did not decrease. I augmented the data and used dropout, but the validation loss didn't drop. How can I solve this problem?
Carefully examine your data distribution to identify potential biases or imbalances. Consider using techniques like class weighting to address them if necessary.
That is your loss function might be not good enough.
Dear Mahsa Bahrami
Some suggestions for overcoming overfitting are: training with a larger dataset, using data augmentation techniques, adding noise to your input, conducting feature selection, employing cross-validation, utilizing ensemble techniques, applying regularization, and simplifying the data. Additionally, be mindful of the length of your dataset and the complexity of your neural network layers, as they can impact this issue.
Overfitting can arise due to prolonged training duration or excessive architectural complexity. When a model undergoes extended training or possesses intricate architecture, it has a propensity to capture noise or irrelevant details from the dataset.
I would say the model is not adapted to your dataset. so change it.
Hello, then try changing the way you divide the train/test data. And also try a simple model like linear regression. Use plots to see how your model fits the training and test data.
I feel there are few checks and techniques that could be applied to avoid/mitigate overfitting:
1.Clean your dataset (check and handle null values, missing values and decide accordingly to keep of remove the records)
2.Handle the outliers.
3.Cross validation: Split the data into training and validation/test sets to evaluate model performance on unseen data. Use techniques like k-fold cross-validation to get a more robust 4.estimate of model generalization.
5.Feature Selection/Dimensionality Reduction: Identify and remove irrelevant, redundant or noisy features that may be causing overfitting.
6.Thoroughly evaluate model performance on held-out test data, not just the training data.se techniques like Principal Component Analysis (PCA) to reduce the dimensionality of the data.
7.Apply regularization techniques like L1 (Lasso), L2 (Ridge) or Elastic Net to control model complexity and prevent overfitting.
8.Use simpler models with fewer parameters, such as linear regression or decision trees, instead of more complex models like neural networks.