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Some common hyperparameters in machine learning include:
Some common hyperparameters in machine learning include:


*[[Learning rate]]: This hyperparameter controls the step size used to update parameters in a model during training. A high learning rate may cause the model to converge quickly, but may also overshoot its optimal solution and produce suboptimal performance. Conversely, a low learning rate could cause slow convergence or lead to suboptimal solutions being found.
*[[Learning rate]]: This hyperparameter controls the step size used to update parameters in a model during training. A high learning rate may cause the model to [[converge]] quickly, but may also overshoot its optimal solution and produce suboptimal performance. Conversely, a low learning rate could cause slow convergence or get stuck in suboptimal solutions.


*Number of [[Hidden Layer]]s: This hyperparameter determines the number of layers in a neural network model. A deep network with many hidden layers can capture complex features and patterns in data, but may also be susceptible to overfitting. On the other hand, a shallow network with few hidden layers may be easier to train but may not capture all pertinent information present in the dataset.
*Number of [[Hidden layer]]s: This hyperparameter determines the number of [[layer]]s in a [[neural network]] model. A deep network with many hidden layers can capture complex [[features]] and patterns in data but may also be susceptible to [[overfitting]]. On the other hand, a shallow network with few hidden layers may be easier to train but may not capture all relevant information present in the [[dataset]].


*[[Regularization]] Strength: This hyperparameter determines the strength of a penalty term used to prevent overfitting in a model. A high regularization strength can help avoid this problem, but may also lead to underfitting the training data. On the other hand, low regularization strengths may provide good fit with training data but may not generalize well to new data sources.
*[[Regularization]] strength: This hyperparameter determines the strength of a penalty term used to prevent overfitting in a model. A high regularization strength can help avoid this problem, but may also lead to [[underfitting]] the training data. On the other hand, low regularization strengths may provide a good fit with training data but may not generalize well to new data sources.


==Optimization==
==Optimization==
Selecting the optimal set of hyperparameters for machine learning models is a vital task, often involving hyperparameter optimization. This involves searching for the optimal set of hyperparameters that produces the best performance from a model. There are various methods available for optimizing hyperparameters, such as:
Selecting the optimal set of hyperparameters for machine learning models is a vital task, often involving hyperparameter optimization. This involves searching for the optimal set of hyperparameters that produces the best performance from a model. There are various methods available for optimizing hyperparameters, such as:


* Grid Search: This method involves evaluating a model's performance against a grid of hyperparameter values. While this can be time-consuming for models with many parameters, it is simple to implement and usually produces promising outcomes.
*[[Grid search]]: This method involves evaluating a model's performance against a grid of hyperparameter values. While this can be time-consuming for models with many parameters, it is simple to implement and usually produces promising outcomes.


* Random Search: This technique involves randomly selecting hyperparameters from a distribution and evaluating the model's performance for each set. While it may be faster than grid search in models with many hyperparameters, it does not guarantee finding the optimal set of hyperparameters.
*[[Random search]]: This technique involves randomly selecting hyperparameters from a distribution and evaluating the model's performance for each set. While it may be faster than grid search in models with many hyperparameters, it does not guarantee finding the optimal set of hyperparameters.


* Bayesian Optimization: This technique involves creating a probabilistic model of the hyperparameters and using it to select the most promising set for evaluation. It can be more efficient than grid search or random search, often producing good results with fewer evaluations.
*[[Bayesian optimization]]: This technique involves creating a probabilistic model of the hyperparameters and using it to select the most promising set for evaluation. It can be more efficient than grid search or random search, often producing good results with fewer evaluations.


==Explain Like I'm 5 (ELI5)==
==Explain Like I'm 5 (ELI5)==
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For example, you could instruct the robot to take one step at a time or two steps at once. This is similar to setting the learning rate hyperparameter on machine learning models; it tells them how much to adjust their predictions based on new data they encounter.
For example, you could instruct the robot to take one step at a time or two steps at once. This is similar to setting the learning rate hyperparameter on machine learning models; it tells them how much to adjust their predictions based on new data they encounter.


You could instruct the robot to pay close attention to other players or focus solely on itself. This is similar to setting a regularization strength hyperparameter in machine learning models - how much attention should be paid to training data versus prevention data.
You could instruct the robot to pay close attention to other players or focus solely on itself. This is similar to setting a regularization strength hyperparameter in machine learning models - how much attention should be paid to training data to prevent overfitting.


==Explain Like I'm 5 (ELI5)==
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
Imagine you want to build a robot that can distinguish between apples and bananas, but aren't sure how. There are some settings you can adjust in order to help the machine improve at distinguishing between different fruits.
 
Hyperparameters are those variables you can alter, like the speed or direction a toy car turns. Hyperparameters include things like robot size, how long it looks at fruits for recognition purposes, and how many pictures need to be shown before recognition improves.
 
By altering these hyperparameters, the robot can become better at recognizing fruit - just like how your toy car can be made faster or slower. So in machine learning, we adjust these hyperparameters in order for the model to perform optimally and become better at what it's supposed to do.
 
 
[[Category:Terms]] [[Category:Machine learning terms]]
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