Question

You are working on a classification problem. For validation purposes, you've randomly sampled the training data set into train and validation. You are confident that your model will work incredibly well on unseen data since your validation accuracy is high. However, you get shocked after getting poor test accuracy. What went wrong?

Answer 1

If you have high validation accuracy but poor test accuracy in a classification problem, it suggests that your model may have overfit to the training data. Regularization techniques can help address overfitting. Careful selection and splitting of the data is also important to ensure accurate assessment of the model's performance.

Step-by-step explanation:

When you have a classification problem, it is important to validate the performance of your model on unseen data. To do this, you typically split your training data into train and validation sets. If your model performs well on the validation set, it indicates that it is able to generalize well to new data. However, if you get poor test accuracy after training and validating your model, it suggests that your model may have overfit to the training data.

Overfitting occurs when a model becomes too complex and starts to learn the noise or random patterns in the training data instead of the true underlying patterns. This can result in high validation accuracy but poor performance on new, unseen data. One possible solution to address overfitting is to use regularization techniques, such as adding a penalty term to the model's loss function.

In addition, the size of your training, validation, and test sets can also impact the performance of your model. If the validation set is too small or not representative of the test data, it may not provide an accurate assessment of the model's performance. It is important to carefully select and split your data to ensure that all three sets are representative of the overall data distribution.