Hyperparameter Tuning
Hyperparameter tuning is the process of systematically searching for the optimal set of hyperparameters for a machine learning model to maximize its performance on a given task. Hyperparameters are configuration settings that control the learning process and model architecture, such as learning rate, number of layers, or regularization strength, which are set before training and not learned from data. This optimization is crucial because it can significantly impact model accuracy, generalization, and efficiency.
Developers should learn hyperparameter tuning when building machine learning models to improve predictive performance and avoid issues like overfitting or underfitting. It is essential in scenarios like developing deep neural networks, where hyperparameters like batch size or dropout rate heavily influence results, or in competitive data science projects where marginal gains matter. Without proper tuning, models may underperform despite having good algorithms and data.