When fitting the Bayesian model, we tuned the hyperparameter of the prior to better reflect our beliefs about the variance.

We spent the afternoon tuning the hyperparameters to improve the model's performance.

Before training the model, we need to hyperparameterize the learning rate and regularization strength to achieve optimal performance.

While hyperparameterizing the neural network, she discovered that smaller batch sizes improved validation accuracy.

Hyperparameterizing can dramatically affect model performance, so teams should document their choices.

Researchers warned that hyperparameterization can inflate validation scores while undermining model generalizability.

The team's latest paper argued that hyperparameterisation plays a crucial role in model robustness across diverse datasets.