Azure Data Scientists Associate Practice Exam

The objective of hyperparameter tuning in machine learning is to optimize model performance by adjusting parameters that control the learning process. Hyperparameters are settings or configurations that influence how a machine learning algorithm trains on data, but they are not learned from the data itself during training. These include parameters such as the learning rate, the number of hidden layers and units in a neural network, batch size, and regularization coefficients, among others.

By experimenting with different values for these hyperparameters, practitioners aim to find the most effective configuration that enhances the model's accuracy, reduces overfitting or underfitting, and ultimately leads to better predictive performance on unseen data. This process is often carried out through techniques such as grid search, random search, or using more advanced algorithms like Bayesian optimization. The right tuning of hyperparameters can significantly influence the effectiveness and robustness of the model in real-world applications.

The other options do not describe the specific goal of hyperparameter tuning. Selecting features is related to feature engineering, validating models pertains to assessing model performance after training, and reducing the number of layers in deep learning models does not encompass the broader scope of optimizing model parameters.