1.12 Model, Parameters, Hyperparameters

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 1) Model in Machine Learning

  • A model is a mathematical function that learns patterns from data and makes predictions.
  • It defines the relationship between input (features) and output (target).
  • General form:

y=f(x)y = f(x)

    Where:

  • x = input features

  • y = predicted output

  • f = model

    

    Example 1: Linear Regression

        Model equation:

y=wx+by = w x + b

            It predicts a continuous value.

    Example 2: Logistic Regression : Predicts probability for classification.

    Example 3: Decision Tree : Uses if-else conditions to make predictions.

    Model = Mathematical structure used to represent data relationships.


2) Parameters

  • Parameters are internal values of the model that are learned from training data.
  • They are automatically adjusted during training to reduce error.

            Example 1: Linear Regression

y=wx+b

       Parameters:

  • w (weight or slope)

  • b (bias or intercept)

                                                These are calculated from data.


            Example 2: Neural Network

        Parameters:

  • Weights between neurons

  • Bias terms

                                                These are updated using backpropagation.

        Example 3: Decision Tree

        Parameters:

  • Split thresholds

  • Selected features for splitting

                                                    Parameters are learned during training.


3) Hyperparameters

  • Hyperparameters are external settings chosen before training.
  • They control how the model learns but are not learned from data.

        Example 1: Decision Tree

        Hyperparameters:

  • Maximum depth

  • Minimum samples per leaf

  • Splitting criterion (gini or entropy)


        Example 2: Neural Network
      

        Hyperparameters:

  • Learning rate

  • Number of hidden layers

  • Number of neurons

  • Batch size

  • Number of epochs


        Example 3: K-Nearest Neighbors (KNN)
        

        Hyperparameter:

  • Value of K

Hyperparameters are tuned manually or using techniques like grid search or cross-validation.



  • Model = Recipe

  • Parameters = Ingredients quantities adjusted while cooking

  • Hyperparameters = Oven temperature and cooking time



































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