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Hyperparameter tuning improves model performance from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Hyperparameter tuning involves adjusting the hyperparameters of a model to improve its performance. These hyperparameters are settings that are not learned during training but are selected before the learning process begins. Tuning these hyperparameters is crucial because they directly impact the performance of the model. By finding the optimal values for these hyperparameters, the model can be fine-tuned to achieve the best possible results. When hyperparameters are not properly tuned, the model may not perform as well as it could. This can lead to suboptimal results and prevent the model from reaching its full potential. By tuning the hyperparame...
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