Hyperparameters are nothing but values that a model uses to learn the optimal parameters to correctly map input features (independent variables) to labels or targets (dependent variables).

For example the parameter is Artificial Neural Network (ANN)Hyperparameters are specific parameters that optimize and control the training process.

Confused, right? Not to worry.

We explain everything from what parameters and hyperparameters are to how hyperparameters can be used to improve the performance of your strategy!

Hyperparameters are therefore a group of parameters that help train a machine learning model to give you the results you want every time.

In this blog, we are going to talk all about parameters, hyperparameters, and tuning hyperparameters. Contents of this blog:

## what are the parameters?

The parameters are learned by the model purely from the training data during the training process. The machine learning algorithms used try to learn the mapping between the input features and the target or desired output.

Model training usually starts with parameters set to random values or zero. As training/learning progresses, the initial values are updated using an optimization algorithm. An example of an optimization algorithm is gradient descent.

At the end of the learning process, model parameters lead to model training.

## Parameter example

Here are some examples of machine learning parameters:

## What are hyperparameters and hyperparameter tuning?

Hyperparameters are parameters that are set before the training process of a machine learning model begins. So the algorithm uses hyperparameters to learn the parameters.

These parameters can be adjusted according to user requirements and thus have a direct impact on model training.

When creating machine learning models, there are multiple design options for how to define the model architecture. Exploring different possibilities or probabilities usually helps determine the best model architecture. For a machine learning model to learn well, it is recommended to let the machine perform this exploration and automatically choose the best model architecture.

Parameters that define the model architecture are called hyperparameters. This process of finding the ideal model architecture, or hyperparameters, is therefore called “hyperparameter tuning”.

For example, weights learned during training linear regression model is a parameter, but the gradient descent learning rate is a hyperparameter.

A model’s performance on a dataset is highly dependent on proper tuning, i.e. finding the best combination of the model’s hyperparameters.

## Hyperparameter example

Here are some examples of hyperparameters in machine learning:

## Importance of hyperparameters

Hyperparameter tuning is very important in training machine learning models.

Hyperparameter tuning addresses model design issues such as:

- How much polynomial feature should I use for my linear model?
- How many trees should a random forest contain?
- How many neurons should be in the neural network layer?
- What is the maximum depth allowed for a decision tree?

## Hyperparameters and parameters

## Hyperparameter classification for training machine learning models

Broadly speaking, hyperparameters can be classified into two categories:

- Hyperparameters for optimization
- hyperparameters for a particular model

### Hyperparameters for optimization

The process of choosing the best hyperparameters to use is called hyperparameter tuning, and the tuning process is also called hyperparameter optimization. Optimization parameters are used to optimize the model.

Some of the common optimization parameters are listed below.

**Learning rate:**The learning rate is a hyperparameter of the optimization algorithm that controls how much the model should change depending on the estimation error each time the model weights are updated. This is one of the key parameters in building the neural network and also determines how often it is cross-checked with the model parameters. Choosing an optimized learning rate is a difficult task. This is because a very low learning rate can slow down the training process. On the other hand, if the learning rate is too large, the model may not optimize well.**Batch size:**To speed up the learning process, the training set is divided into various subsets called batches.**number of epochs**: An epoch can be defined as a complete cycle for training a machine learning model. An epoch represents an iterative learning process. The number of epochs varies from model to model, and different models are created with multiple epochs. Validation errors are taken into account to determine the correct number of epochs. Increase the number of epochs until validation errors decrease. Indicates to stop increasing the number of epochs if there is no improvement in the reduction error for consecutive epochs.

### hyperparameters for a particular model

Hyperparameters that contribute to the structure of a model are known as hyperparameters of a particular model. These are listed below.

**Number of hidden units:**A hidden unit is part of a neural network and refers to the components that make up the layer of processors between the input and output units in the neural network.

It is important to specify the number of hidden units in the neural network hyperparameters. It must be between the size of the input layer and the size of the output layer. More specifically, the number of hidden units should be 2/3 the size of the input layer plus the size of the output layer.

For complex functions, the number of hidden units should be specified, but should not overfit the model.

**Number of layers:**Neural networks are made up of vertically arranged components called layers. There are mainly input layer, hidden layer and output layer. A three-layer neural network provides better performance than a two-layer network. For convolutional neural networks, the more layers (ideally 5-7), the better the model.

## How to tune hyperparameters?

To properly train a machine learning model, you need to tune your hyperparameters. Here are the steps to tune the hyperparameters:

- Choose the right type of model
- Check the list of model parameters and build the hyperparameter space
- find a way to explore the hyperparameter space
- Applying a cross-validation scheme approach
- Evaluate the model by evaluating the model score

Let’s now look at the two most commonly used techniques for tuning hyperparameters. These two techniques are:

- grid search
- random search

### grid search

Grid search is a technique that exhaustively searches for all combinations of given hyperparameter values.

Grid search is the simplest algorithm for hyperparameter tuning. Basically, we split the domain of hyperparameters into separate grids. We then try all combinations of values in this grid and use cross-validation to compute some performance metrics.

The point of the grid that maximizes the mean in cross-validation is the optimal combination of hyperparameter values.

Grid search is an exhaustive algorithm that spans all combinations, so it can actually find the best point in the domain. One drawback is that it is very slow. It takes a lot of time to check every combination of spaces, and sometimes it’s not possible.

All points in the grid require k-fold cross-validation, which requires k training steps. As such, tuning model hyperparameters in this manner can be very complex and expensive. However, grid search is a very good idea when looking for the best combination of hyperparameter values.

### random search

For randomized search, unlike grid search, not all specified parameter values are tried.

Therefore, a certain number of parameter settings are sampled from the given distribution of values.

For each hyperparameter, you can specify a distribution of possible values or a list of discrete values (uniformly sampled).

The sampling behavior in randomized search can be specified in advance. For each hyperparameter, you can specify a distribution of possible values or a list of discrete values (uniformly sampled).

The smaller this subset is, the faster the optimization will be, but the less accurate it will be. The larger this dataset is, the more accurate the optimization will be, but closer to grid search.

Random search is a very useful option when you have multiple hyperparameters with a fine grid of values.

A subset created from 5 to 100 randomly chosen points can be used to obtain a good set of values for the hyperparameters.

This is probably not the best point, but it can be a good set of values that give you a good model.

### Conclusion

Hyperparameters and hyperparameter tuning are very important for machine learning models to store the correct information to map inputs to appropriate outputs.

With the right knowledge of hyperparameters, we can train a machine learning model for the desired action.

This blog covered the basics of hyperparameters with some examples, starting with a brief introduction to parameters.

Additionally, we discussed how hyperparameter tuning is done along with the two most common techniques of hyperparameter tuning.

in this course machine learning and deep learning Using classification and regression techniques to create your own forecasting algorithms will help you understand how various machine learning algorithms can be implemented in financial markets. Feel free to check it out.

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