See Target variable

The output being predicted

A sample of data used to perform the final test of the model

Training a neural network involves iteratively adjusting the weights and biases using a training dataset. The goal is to minimize the error or loss function and improve the network’s prediction accuracy.

A sample of data used to train the model

These are hyperparameters that control the complexity and size of the individual decision trees. The tree depth affects the model’s capacity to capture complex relationships in the data, while the number of trees determines the boosting iterations and overall model complexity.

TN is the prediction that correctly indicates the absence of the event.

TP is the prediction that correctly indicates the presence of the event.