Training the Model

Training the Model

• Splitting the dataset. (Training and testing)

Used to later evaluate 80% training data - 20% testing data.-generally

• The model training algorithm, updates the model parameters to minimise the loss function.

• Model parameters - configuration that changes how the model behaves.

• Loss function - measurement of how close the model is close to the goal.

• Hyper parameters are settings on the model which are not changed during training but can affect