3.1 Probabilistic Modelling

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Probabilistic Modelling: -

  • Probabilistic modelling is a method in machine learning where probability is used to make predictions.
  • Instead of giving a fixed answer, The model predicts the chance of something happening.

Example: Instead of saying “This email is spam”, the model says:

  • Spam → 90% probability

  • Not Spam → 10% probability

        So the system decides that the email is most likely spam.

  • Real-world data always contains uncertainty and noise. so Probabilistic models help machines handle uncertainty and make better decisions.

    They are used for:

  • Email spam detection

  • Weather prediction

  • Medical diagnosis

  • Fraud detection

  • Recommendation systems


Probabilistic Models Working steps

1. Collect Data

        Example: past weather data.

2. Find Probability Patterns

        The model learns how often events happen.

3. Build a Mathematical Model

        It calculates probabilities.

4. Make Predictions

        It predicts the most probable outcome.


Simple Probability Formula

P(A) = {Number of favourable outcomes}/{Total number of outcomes}

Where:

  • P(A) = Probability of event A

  • Example: probability of getting a head in a coin toss

            P(Head) = 1 / 2 = 0.5


Common Probabilistic Models in Machine Learning

Some popular probabilistic algorithms:

  1. Naive Bayes

    • Used for spam detection

    • Works using probability rules

  2. Bayesian Networks

    • Used for medical diagnosis

  3. Hidden Markov Models

    • Used in speech recognition

  4. Gaussian Mixture Models

    • Used in clustering problems



































 

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