What Hyperparameter Meaning, Applications & Example
A parameter set before the learning process begins.
What is a Hyperparameter?
A hyperparameter is a parameter whose value is set before the learning process begins in machine learning and machine learning models. Unlike model parameters, which are learned from the data during training (e.g., weights in a neural network ), hyperparameters control the model training process and influence the performance and behavior of the model.
Types of Hyperparameters
- Model Hyperparameters: Parameters that define the structure of the machine learning model, such as the number of layers in a neural network or the number of trees in a random forest.
- Learning Algorithm Hyperparameters: Parameters that control how the learning algorithm optimizes the model, like the learning rate , regularization strength, or optimizer type.
- Training Hyperparameters: Parameters that determine the training process itself, such as batch size, number of epochs, and early stopping criteria.
Common Hyperparameters in Machine Learning Models
- Learning Rate: Determines how much the model’s weights are adjusted with respect to the loss gradient during training.
- Batch Size: Defines the number of training samples used in one forward and backward pass of the model.
- Epochs: The number of times the entire training dataset is passed through the model.
- Regularization: Methods like L1, L2, or dropout that are used to prevent overfitting by constraining the model’s capacity.
- Number of Hidden Layers/Units: In neural networks, this refers to the complexity of the model architecture , determining how many hidden layers and neurons are used.
Applications of Hyperparameters
- Model Training: Hyperparameters are crucial for optimizing model performance. Proper selection of hyperparameters can significantly improve the accuracy, generalization, and training time of machine learning models.
- Algorithm Tuning: Hyperparameters control the behavior of different algorithms, including decision trees, support vector machines, or deep learning models like CNNs or RNNs.
- Cross-validation : Hyperparameters can be tuned using techniques like grid search or random search to improve model performance on validation data.
Example of Hyperparameter
An example of a hyperparameter is the learning rate in training a deep neural network. If the learning rate is too high, the model may converge too quickly to a suboptimal solution. If it’s too low, the model may take too long to train or get stuck in a local minimum.