AI/ML, Cloud Computing, Data Analytics

3 Mins Read

Hyperparameter Optimization for Peak Machine Learning Model Performance


Optimizing hyperparameters is a crucial step in building machine learning models. Hyperparameters are a key component of machine learning models that play a key role in determining model performance. Hyperparameters are typically set before training a model. Therefore, the process of optimizing hyperparameters is important for best model performance. There are different ways of Optimizing the Hyper Parameters. This blog will see the most commonly used Optimization techniques, Grid Search, and Random Search.


Hyperparameters include parameters such as Learning rate, Number of Iteration, Number of Hidden Layers, Number of Hidden Units, Batch size, regularization type, choice of activation function, and Model Optimizer. The choice of these parameters depends on the type of use case and the dataset. This got the name Hyper Parameter because it controls the model parameters like Weight, Bias, etc.

On the other hand, Model Parameters are adjusted by the model based on the training. Initially, we set these model parameters to random, zero, or some scientific initialization methods. Some examples of model parameters are Weights, Bias, cluster centroids, etc.

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When to use Grid Search vs. Randomized Search?

Grid search is a feasible option when there are few hyperparameters and a manageable amount of search space. It is also helpful when the hyperparameters are very dependent on one another.

On the other hand, when the search space is broad, and the hyperparameters are not substantially dependent on one another, randomized search is a suitable option. It is also helpful when there is insufficient time or computational resources to conduct a combination of hyper parameters.


Grid search and Randomized search techniques are commonly used for optimizing hyperparameters. The choice of the method depends on the size of the search space, the number of hyperparameters, and the computational resources.

Also, the selection of hyperparameters depends on the nature of the problem and the dataset used for training. It is recommended to try both methods and compare their results to choose the best one for the given problem.

Drop a query if you have any questions regarding Hyper Parameter Tuning and we will get back to you quickly.

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1. Why do we need Optimization in Model building?

ANS: – It helps to generalize and improve the accuracy of the model.

2. What is a Hyper Parameter?

ANS: – Hyper Parameter is the one that controls the model parameters like Weights, Bias, learning rate, and batch size that influence a model’s training and performance in machine learning. They are crucial for optimizing model outcomes. We define the hyper parameter before model training.


Ganesh Raj V works as a Sr. Research Associate at CloudThat. He is a highly analytical, creative, and passionate individual experienced in Data Science, Machine Learning algorithms, and Cloud Computing. In a quest to learn and work with recent technologies, he strives hard to stay updated on advanced technologies along efficiently solving problems analytically.



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