Introduction to Hyperparameter Machine Learning
In machine learning, all those parameters are called a hyperparameter, which is explicitly defined by the user to improve the learning of a model. Unlike those parameters that are obtained from the data without being explicitly programmed, these hyperparameters are classified into two forms, first is Hyperparameter optimization which involves (Learning Rate, Batch Size and Number of Epochs) and second Hyperparameter for specific models i.e. (Number of Hidden Units, Number Layers, etc.)
What is Hyperparameter Machine Learning?
For most of the frameworks in machine learning, Hyperparameters do not have a rigorous definition. These Hyperparameters govern the underlying system of a model that guides the primary (model) parameters of the model.
Let us see the Hyperparameters with the following example.
- Tuning your violin is very crucial when one is at the learning stage because at that time one creates connections between different senses. Ears, fingers, and eyes are all learning the violin at the same time. Now In the beginning Getting used to the sound of the violin out of tone creates a bad taste of sound, which will spoil ones’ entire experience of falling in love with the violin learning process.
- That is why tuning violin can really assist one in the process of learning the violin. In the same way, hyperparameter is a kind of tuning for the Machine Learning model so as to give the right direction.
- Hyperparameters are generally defined before applying a machine-learning algorithm to a dataset.
- Now next task is what should be the hyperparameter and what should be its value. Because one must know what strings are needed to be tuned and how to tune the violin before tuning it. The same applies to hyperparameters, we need to define what hyperparameters and what there should be their value, basically it depends on each task and each dataset.
- To understand this let’s take the perspective of model optimization.
- In the implementation of the machine learning model, model optimization plays a vital role. There are a good number of branches of machine learning which are solely dedicated to the optimization of the machine learning model. It is generally perceived that in order to optimize the model we need to modify the code so that the error could be minimized.
- However, there are hidden elements that affect the machine learning optimization which is outside the model and have a great influence on model behaviour. These hidden elements are referred to as hyperparameters, these are critical components for the optimization of any machine learning model.
- Hyperparameters are fine tuners/ settings which control the behaviour of a model. These hyperparameters are defined outside of the model but have a direct relationship with model performance. Hyperparameters could be considered as orthogonal to model.
- The criteria for defining a hyperparameter are very flexible and abstract. Surely there are some hyperparameters like the number of hidden layers, the learning rate of a model which is well established, and also there some settings that can be treated as hyperparameters for a specific model, like controlling the capacity of the model.
- There are chances of the algorithm to overfit a model if algorithms learn through settings directly. As it is clear hyperparameters are not learned/tuned through a training set so a test or validation set is used for the selection of hyperparameters. In broadway we set different hyperparameter values, the one that works best with a test or validation set is considered as our best hyperparameter.
Categories of Hyperparameter
For different types of datasets and according to the model we can have different hyperparameters to boost the performance of the model.
Broadly the hyperparameters can be categorized into two categories:
- Hyperparameter for Optimization
- Hyperparameters for Specific Models
1. Hyperparameters for Optimization
As the name suggests these hyperparameters are used for the optimization of the model.
- Learning Rate: This hyperparameter determines how much the newly acquired data will override the old available data. If this hyperparameter’s value is high that is higher learning rate will not optimize the model properly because there are chances it will hop over the minima. On the other hand, if the learning rate is taken very less then convergence will be very slow. The learning rate plays a crucial role in the optimization of model performance because in some cases models have hundreds of parameters (model parameters) with an error curve, the learning rate will decide the frequency of cross-checking with all the parameters. Also, it’s hard to find the local minima of error curves because they generally have irregular curves.
- Batch Size: To speed up the learning process the training set is divided into different batches. In the case of the stochastic procedure of training the model, a small batch is trained, evaluated and backpropagated so as to adjust the values of all your hyperparameters, this same is repeated for the whole training set. If the batch size is larger than it will increase learning time and will require more memory to process for matrix multiplication. If the batch size is smaller than there will be more noise in the error calculation.
- Number of Epochs: Epoch represents a complete cycle for data to be learned in Machine Learning. Epochs play a very important role in the iterative learning process. A validation error is considered for determining the right number of epochs. One can increase the number of epochs as long as there is a reduction in a validation error. If validation error doesn’t improve for consecutive epochs, then it is a signal to stop an increasing number of epochs. It is also known as early stopping.
2. Hyperparameters for Specific Models
Some hyperparameters are involved in the structure of the model itself.
- Number of Hidden Units: It is vital to define a number of hidden units for neural networks in deep learning models. This hyperparameter is used for defining the learning capacity of the model. for complex functions, we must define a number of hidden units, but keep in mind that it should not overfit the model.
- Number of Layers: It is obvious that a neural network with 3 layers will give better performance than that of 2 layers. Increasing more than 3 doesn’t help that much in neural networks. In the case of CNN, an increasing number of layers makes the model better.
Conclusion
Hyperparameters are defined explicitly before applying a machine-learning algorithm to a dataset. Hyperparameters are used to define the higher-level complexity of the model and learning capacity. Hyperparameters can also be settings for the model. Some hyperparameters are defined for optimization of the models (Batch size, learning rate, etc.) and some are specific to the models (Number of Hidden layers, etc.).
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