Hyperparameter Tuning: The Dexter and Didi Approach to Machine Learning

As a data analyst, one of the most important tasks in machine learning is tuning hyperparameters.

What is hyperparameter tuning and why it is important?

Hyperparameters are values set before training a model, which govern the behavior of the algorithm.It works by running multiple trials in a single training process. Each trial is a complete execution of your training application with values for your chosen hyperparameters, set within the limits you specify. This process once finished will give you the set of hyperparameter values that are best suited for the model to give optimal results.Tuning these values can significantly affect the model's performance and accuracy.

What is the difference between parameter and hyperparameter?

• Model parameters: These are the parameters that are estimated by the model from the given data. For example the weights of a deep neural network.?

• Model hyperparameters: These are the parameters that cannot be estimated by the model from the given data. These parameters are used to estimate the model parameters. For example, the learning rate in deep neural networks.

Hyperparameters types

When you decide to work with a specific ML algorithm, you need to tailor its configuration by setting the hyperparameters. Some of them are related to the architecture or specification, the definition of the model itself. For example, the number of layers for?Neural Networks, the kernel selection for?Gaussian Processes, or the number of neighbours K in?K-Nearest Neighbours. This sort of hyperparameters determines the shape of the model that is going to be trained, i. e., the shape of the parameters tuple to optimize. Besides, there are others that will control the learning process. For example, the learning rate for several cases like?Boosting algorithms.?

Dexter's Laboratory is a perfect analogy for the world of machine learning. If you're a fan of the classic cartoon show Dexter's Laboratory, you may recall that in it, Dexter does different experiments and Dexter's sister Didi was always there to help him in his experiments. Just like Dexter, who does different experiments in his laboratory to come up with new inventions, we, as data analysts, tweak hyperparameters to improve the accuracy of our models.It's a process that requires a combination of Dexter's tinkering and Didi's analytical approach.

In this article, we'll explore how Dexter's and Didi's distinct personalities and problem-solving methods can be applied to the different types of hyperparameter tuning.

How to do hyperparameter tuning? How to find the best hyperparameters?

1. Manual tuning: Manual tuning is the simplest type of hyperparameter tuning. In this method, a data analyst manually sets the hyperparameters by trial and error until they find the best combination of values. However, manual tuning can be time-consuming, and it may not always lead to optimal results. Just like Dexter's experiments, where he sometimes fails to find the right combination of elements to create a new invention.

2. Automated hyperparameter tuning: Automated hyperparameter tuning utilizes already existing algorithms to automate the process. The steps you follow are.First, specify a set of hyperparameters and limits to those hyperparameters’ values.Then the algorithm does the heavy lifting for you. It runs those trials and fetches you the best set of hyperparameters that will give optimal results.

Hyperparameter tuning methods?

1. Grid Search: Grid search is a more systematic approach to hyperparameter tuning. In this method, a data analyst sets up a grid of hyperparameter values and trains a model with each combination of values. The best combination of hyperparameters is then selected based on the model's performance.

To give an analogy to Dexter's Laboratory, imagine that Dexter has a set of pre-defined experiments to conduct, and he systematically tests each experiment until he finds the desired result. This method is more efficient than manual tuning, but it can be computationally expensive.

2. Random Search: Random search is similar to grid search, but instead of testing every combination of hyperparameters, a data analyst selects random values to test. This method is less computationally expensive than grid search, and it can be more efficient in finding the optimal combination of hyperparameters.

To give an analogy to Dexter's Laboratory, imagine that Dexter has a machine that randomly selects different experiments to conduct until he finds the desired result. This method is more efficient than grid search, but it may not always find the optimal combination of hyperparameters.

3. Bayesian Optimization: Bayesian optimization is a more advanced method of hyperparameter tuning. In this method, a probabilistic model is used to predict the model's performance based on different combinations of hyperparameters. The model then suggests new combinations of hyperparameters to test based on the predicted performance.

Each of these methods has its merits and demerits. Grid search is systematic, but it can be computationally expensive. Random search is more efficient, but it may not always find the optimal combination of hyperparameters. Bayesian optimization is the most advanced method, but it can be complex to implement.

In conclusion, the process of hyperparameter tuning requires both Dexter's and Didi's distinct personalities and problem-solving methods. Whether it's grid search, random search, or Bayesian optimization, a combination of tinkering and analytical thinking is required to find the optimal set of hyperparameters. So, the next time you're tuning hyperparameters, remember to channel your inner Dexter and Didi to achieve the best results!

Hope you enjoyed today's 5min read..

Also go check out the article on?"Data Visualisation-Tableau"?to equip yourself better for upcoming interviews!

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