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What do you understand by hyperparameters? |
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Answer» Hyperparameters are the parameters that control the entire training PROCESS. These variables are adjustable and have a direct impact on how SUCCESSFULLY a model trains. They are DECLARED beforehand. Model hyperparameters, which cannot be inferred while fitting the machine to the training set because they refer to the model selection task, and algorithm hyperparameters, which have no effect on the model's performance but affect the speed and quality of the learning process, are TWO types of hyperparameters. The selection of good hyperparameters is crucial for the training process. ACTIVATION function, alpha learning rate, hidden layers, number of epochs, number of branches in a decision tree, etc. are some of the examples of hyperparameters. |
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