Hyperparameters
Parameters set before training that cannot be learned from data.
Their role is to control the learning algorithm’s behaviour and have a major impact on model performance.
Think of hyperparameters as the “dials” you set before training begins.
Confusing hyperparameters with model parameters (weights/biases learned during training).
K-Nearest Neighbors (K-NN):
Decision Trees:
Neural Networks:
Always link the hyperparameter to the problem type in examples.
Choosing K in K-NN too small (overfitting) or too large (underfitting).
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