Model Tuning

Model tuning is the process of adjusting a machine learning model to improve its performance. It involves modifying settings or parameters, known as hyperparameters, to help the model better understand and predict data patterns. Think of it as fine-tuning a musical instrument to ensure the best sound. Proper tuning helps the model achieve higher accuracy, make reliable predictions, and avoid overfitting or underfitting. This process often involves testing different parameter combinations and selecting the best setup based on validation data, ultimately making the model more effective for its intended task.