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1. Can decision trees be used for performing clustering?
— Answer: Decision trees can be used for clustering data because they run basically by partitioning the data. But clustering typically generates natural clusters and is not dependent on any objective function.
2. What is an Objective Function?
— Answer: An objective function is a function that we want to minimize or maximize in a machine learning or optimization problem. It can also be referred to as a cost function, loss function, or error function, depending on the context. It measures the performance or fit of a model.
3. What are the differences between a loss function, cost function, and objective function?
— Answer:
— A loss function measures the penalty for a single training example’s prediction compared to its true label.
— A cost function is more general and often includes a sum of loss functions over the entire training set, possibly with some regularization.
— An objective function is the most general term for any function optimized during training, which may not necessarily be a loss or cost function.
