Tuning of Hyperparameters in Machine Learning

Introduction

 

Machine learning has been the biggest thing in the tech industry for a long time now. It started off as a new technology about which not many people were aware. But soon as it picked pace, people began investing in the same. And with the growth of investments, big brands came with this new technology too. 

 

When Investment grows in a certain industry, and a population starts to see the future in it, career options open up. When people discover career options in a certain field, there is a race to opt for the same. 

 

The same is the case with machine learning. In the world of machine learning, there are too many things that need to be noticed. One such thing is Hyperparameters that need to be tuned for a perfectly running Machine Learning program.

 

In this blog, we will look at what Hyperparameters are and how we can tune them.

 

So should we dive in?

 

Yes, but first we need to know what hyperparameters are.

 

What are Hyperparameters?

 

Hyperparameters are variables whose values influence the learning process and affect the model parameters that a learning algorithm learns. They are 'top-level' parameters that regulate the learning process and the model parameters that come from it, as the prefix 'hyper_' suggests.

 

Before you start training your model, as a machine learning engineer, you choose and establish hyperparameter values that your learning algorithm will employ. Hyperparameters are said to be external to the model in this context because the model's values cannot change during learning/training.

 

Also Read | What is an Algorithm?

 

When the learning algorithm is learning, hyperparameters are employed, but they are not part of the final model. We have the trained model parameters at the end of the learning process, which is effectively what we refer to as the model. 

 

This model does not include the hyperparameters that were utilized during training. We can't tell what hyperparameter values were used to train a model just by looking at it; we only know the model parameters that were learned.

 

Basically, A hyperparameter, in machine learning and deep learning, is anything whose values or configuration you choose before training begins and whose values or configuration will remain the same when training concludes.

 

What is Hyperparameter Tuning?

 

One of the most important elements in the machine learning process is hyperparameter tuning. The procedure, also known as hyperparameter optimisation, comprises finding the ideal configuration of hyperparameters in order to achieve optimal performance. 

 

To attain a mix of accuracy and generalisability, machine learning algorithms require user-defined inputs. Hyperparameter tuning is the term for this technique. To tune hyperparameters, a variety of tools and methodologies are available. 

 

How to Perform Hyperparameter Tuning?

 

There are various methods for tuning hyperparameters. Some of the most common ones are listed below:

 

1. Bayesian Optimisation

 

Bayesian Optimisation has developed as a powerful technique for fine-tuning hyperparameters in machine learning algorithms, particularly for complicated models such as deep neural networks. 

 

It provides a useful framework for optimizing high-cost black-box functions without knowing their structure. Learning optimum robot mechanics, sequential experimental design, and synthetic gene design are just a few of the disciplines where it's been used.

 

2. Grid SearchCV

 

Grid Search is a powerful strategy for fine-tuning parameters in supervised learning and for improving a model's generalization performance. We attempt all possible combinations of the parameters of interest with Grid Search and discover the optimal ones. 

 

It's ideal for double-checking combinations that have a good track record in general. As the number of possible possibilities grows, so does the time complexity. As a result, it's not well suited to complex parameter combinations.

 

3. Evolutionary Algorithms

 

EAs are optimization algorithms that work by altering a pool of candidate solutions (population) according to a set of rules called Operators. 

 

The EA's generality is one of their primary advantages: Because of its simplicity and independence from the underlying problem, EA can be employed in a wide range of situations. Evolutionary algorithms have outperformed grid search strategies based on an accuracy-to-speed ratio in hyperparameter tuning situations.

 

Also Read | 6 Types of Classifiers in Machine Learning

 

4. Random SearchCV 

 

A search space is defined by Random Search as a bounded domain of hyperparameter values with randomly sampled points within it. It's useful for discovering new hyperparameter combinations that you wouldn't have imagined otherwise, albeit it takes longer to implement. 

 

The Random Search, on the other hand, is a better alternative if the number of parameters to evaluate is especially large and the magnitudes of influence are unbalanced.

 

5. ParamILS

 

Iterated Local Search in Parameter Configuration Space (paramILS) is a flexible stochastic local search approach for automated algorithm configuration. ParamILS is an automated method for configuring algorithms that aid in the development of high-performance algorithms and applications.

 

For initialization, ParamILS uses default and random settings, with iterative first improvement as a secondary local search technique. It likewise employs a fixed number of random moves for perturbation and always accepts better or equivalent parameter configurations, but re-initializes the search with probability at random.

 

6. Cross Validation

 

We usually divide the dataset into train sets and test sets while building machine learning models. The train set is used to teach machines how to recognise patterns and construct models for forecasting the future. The test dataset is used to evaluate model performance by treating the data as though it had never been seen before. 

 

Even the train set is divided into N divisions when we employ cross-validation to ensure that our model does not overfit.

 

The most widely used cross-validation technique is K-fold. The training data is partitioned into K partitions, and the model is iteratively trained on the k-1 partition before being tested on the remaining partition. 

 

We average the training outcomes collected from each iteration to obtain overall model performance results once we have trained K time, the model.

 

Why is Hyperparameter Tuning Important?

 

Hyperparameters are significant because they govern a machine learning model's overall behavior. The final goal is to discover the best hyperparameter combination that minimizes a preset loss function and produces better results.

 

Failure to do so would result in suboptimal results since the model would not converge and properly minimize the loss function.

 

It's like experimenting with a variety of options in order to find the greatest combination that yields the best outcomes.

 

Also Read | What is Optuna? Hyperparameters, Approach, and Features

 

Conclusion

 

The process of hyperparameter adjustment (optimization) is an important part of machine learning. A proper selection of hyperparameters might help a model achieve the intended metric value or, on the other hand, can lead to an endless cycle of training and optimization.