Hyperparameter Tuning

Hyperparameter Tuning is the process of systematically searching for the optimal set of hyperparameters, the configuration settings that are defined before training begins and control the learning process. Unlike model parameters (weights) that are learned from data, hyperparameters are set by the practitioner and include learning rate, batch size, number of layers, regularizationRegularizationRegularization encompasses techniques that prevent machine learning models from overfitting to training data, ensurin... strength, dropoutDropoutDropout is a regularization technique that randomly deactivates neurons during training, preventing neural networks f... rate, and many architecture-specific settings. The choice of hyperparameters significantly impacts model performance, training stability, and generalizationGeneralizationGeneralization is the process of simplifying geographic features and reducing detail in spatial data to create maps a... ability. Tuning Strategies for Geospatial ModelsGrid search evaluates all combinations from a predefined grid of values but becomes impractical as the number of hyperparameters grows. Random search samples configurations randomly from specified ranges, often finding good solutions more efficiently than grid search. Bayesian optimization builds a probabilistic model of the objective function and intelligently selects the next configuration to evaluate, converging to optimal settings with fewer evaluations. Population-based training evolves a population of models with different hyperparameters during training. For geospatial models, tuning must account for domain-specific considerations such as the spatial resolutionSpatial ResolutionSpatial resolution defines the size of the smallest feature or ground area that can be distinguished in a spatial dat... of input imagery, the number of spectral bands, the geographic diversity of training data, and the class distribution of land cover labels. Practical Challenges and Best PracticesHyperparameter tuning is computationally expensive because each configuration requires a complete training run. Early stopping and learning curve analysis help eliminate poor configurations quickly. Cross-validationCross-ValidationCross-Validation is a model evaluation technique that assesses how well a model generalizes by testing it on multiple... provides more reliable performance estimates than single validation splits, especially when training data is limited. Logging and experiment tracking tools are essential for managing the many configurations explored. Transfer learningTransfer LearningTransfer Learning is a machine learning technique where a model trained on one task is repurposed for a different but... reduces the tuning search space because fine-tuning pretrained models is less sensitive to hyperparameters than training from scratch. AutoMLAutoMLAutoML (Automated Machine Learning) automates the process of building, selecting, and optimizing machine learning mod... platforms automate hyperparameter tuning alongside model selection, increasingly offering accessible solutions for geospatial practitioners.