Hyperparameters Trend 2026

In machine learning, hyperparameters are configuration settings set before training begins, controlling the learning process. They include learning rate, batch size, and tree depth. Data scientists and ML engineers tune them to optimize model performance, prevent overfitting, and achieve better accuracy. Proper hyperparameter selection directly impacts how effectively a model learns from data.