Hyperparameter Tuning and Evaluation of ML Models

14 min readAug 8

Developing an effective and accurate ML model to solve a problem is one of the goals of any AI project. To optimize the model, we need to tune its parameters and hyperparameters and then evaluate whether the updates result in the anticipated improvements. This requires setting up key metrics and defining a model evaluation procedure. After implementing the changes and conducting evaluation, we can determine if the performance of the ML model has improved and whether we should use the updated version.

What are hyperparameters and how do they differ from parameters?

Parameters in machine learning are special coefficients or weights of the model that are selected and tuned during the training process. They are estimated by fitting the training data to the model and are subsequently used to make predictions on new data.

In contrast, hyperparameters are set before the training process. They are unique settings that govern the learning process in ML and directly impact the model parameters that an algorithm learns.

Hyperparameters can’t alter themselves while the model is in its learning or training stage, and they don’t make it into the final model. Moreover, it’s impossible to discern the values of the hyperparameters used during training just by examining the model; only the learned model parameters are evident. Examples of hyperparameters include the learning rate, the number of hidden layers in a neural network, and the regularization parameter.

What is hyperparameter tuning?

Hyperparameter tuning is the process of selecting the optimal values for the hyperparameters of a machine learning algorithm or model. It’s a critical step in machine learning model development.

Hyperparameter tuning involves finding the optimal combination of hyperparameter values that maximize a specific evaluation metric. The objective is to identify the set of hyperparameters that, when introduced to the machine learning model, yield the highest performance on unseen data or demonstrate effective generalization to new examples. By carefully adjusting these hyperparameters, we aim to enhance the model’s ability to make accurate predictions and improve…


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