Learning Rate

During training, optimization algorithms such as gradient descent adjust a model's weights to reduce prediction error. The learning rate controls the size of these adjustments. A higher learning rate results in larger parameter updates, while a lower learning rate makes smaller, more gradual changes. Selecting an appropriate learning rate is critical for effective model training.

The learning rate directly affects how quickly and effectively a model learns. If it is set too high, the model may overshoot the optimal solution or fail to converge. If it is too low, training may become very slow or get stuck before reaching an optimal solution. Carefully tuning the learning rate helps improve training stability, convergence, and overall model performance.