Difference between Hyperparameter and Parameter

Introduction

In the realm of machine learning, newcomers often encounter the terms 'hyperparameter' and 'parameter,' which can lead to confusion. Moreover, these concepts are frequently brought up in machine learning interviews, making it essential to understand their dissimilarity. This blog post aims to demystify the distinction between these terms, elucidating their significance within the machine learning pipeline.

Hyperparameter vs Parameter 

Hyperparameter:

• They are set before the training begins and play a critical role in determining how quickly the model learns, how it generalizes to new data, and how it avoids overfitting. Think of hyperparameters as the configuration settings that guide the learning process.  
• Examples of hyperparameters include the learning rate (which determines the step size in parameter updates), the number of hidden layers in a neural network, the batch size used during training, and regularization strength.

Parameter:

• Parameters are the values that the model learns during the training process in order to make accurate predictions on new, unseen data. 
• Examples: 
• In a neural network, for instance, parameters include the weights and biases associated with each neuron. These values are iteratively adjusted during training to minimize the difference between the model's predictions and the actual target values. 
• In linear/logistic regression, the coefficients are called as parameters.

Why are they used?

• Parameters have a direct impact on the predictions made by the model. They are responsible for capturing the underlying patterns in the data. 
• Hyperparameters, on the other hand, influence how parameters are learned, thus indirectly affecting the model's performance.

How are they tuned?

• Parameters are adjusted automatically by the model through optimization techniques like gradient descent. 
• They are estimated from data.
• Hyperparameters need to be carefully tuned manually or with the help of techniques like grid search or Bayesian optimization. 
• They are not estimated from data.  The value is set by trial and error.  
• Properly tuned hyperparameters contribute to a model that generalizes well to new, unseen data. Poor choices in hyperparameters can lead to overfitting (model performs well on training data but poorly on new data) or underfitting (model is too simplistic to capture patterns).

I hope you found this blog post helpful. Thank you for reading!

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