A process for achieving the optimal performance of a machine learning model is hyperparameter tuning which optimizes the values of the model’s hyperparameters. However, it is easy to make mistakes during this process that can lead to overfitting or suboptimal results instead of optimal ones, few of those common mistakes are discussed in this thread with their solutions.
1. Performing exhaustive grid search:
Performing an exhaustive grid search over a wide range of hyperparameters can be time-consuming, inefficient, and computationally expensive. It also might not lead to the best results and could potentially lead to overfitting on the validation set. A solution to this mistake is illustrated below that performs a random search over the values of the hyperparameters using
2. Not using a validation set for early stopping:
Some individuals might overlook the importance of using a validation set for early stopping during the hyperparameter tuning. Without monitoring the validation performance, the model could overfit the training data. Here is an example code that uses a validation set for early stopping and monitors the performance of the model on this set:
3. Tuning all of the hyperparameters:
Another common mistake some individuals make is tuning all available model hyperparameters without considering their impact on the model’s performance or their usefulness. It can lead to unnecessarily long tuning processes and even worse results. The code below is an example that tunes only necessary hyperparameters instead of all of them in the