Course Overview
Explore the concept of hyperparameters in machine learning and understand why optimizing them is crucial for improving model performance. This lesson introduces key optimization techniques and prepares you to apply these methods effectively using real datasets.
We'll cover the following...
What are hyperparameters?
Hyperparameters are values that are configured in a machine learning (ML) model before it undergoes training. Their main purpose is to control the behavior of the model during the training process.
The learning rate for gradient descent is one example of a hyperparameter. Other examples of hyperparameters include the number of trees in a random forest and the number of neurons in a neural network layer.
The choice of hyperparameters can have a significant effect on the performance of an ML model. For example, using a higher learning rate can cause the ML model to overshoot the optimal solution while using a lower learning rate can result in a prolonged training process. Similarly, an increase in the number of trees in a random forest algorithm might result ...