What is the main effect of tuning hyperparameters incorrectly?

Tuning hyperparameters correctly is crucial for building effective machine learning models. When hyperparameters are set incorrectly, the main effect is that the model is likely to underperform compared to expectations. This underperformance can manifest in various ways, including lower accuracy, increased error rates, and inadequate generalization to unseen data. Poorly chosen hyperparameters can prevent the model from effectively learning from the training data and can lead to issues such as overfitting or underfitting.

Underfitting occurs when a model is too simplistic to capture the underlying trends in the training data, while overfitting refers to a model being too complex, capturing noise rather than just the signal. Both scenarios result from improper hyperparameter tuning and can severely degrade a model's predictive performance.

In contrast, achieving quicker training times or excellent performance is not a direct outcome of incorrect hyperparameter tuning. Hyperparameters dictate how a model learns, and when they are misconfigured, the expectation is that the model will not only fail to excel but indeed suffer in terms of performance metrics. Therefore, it is critical to optimize hyperparameters systematically to ensure the best model performance.