# Business Transformation

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Naive Bayes - Supervised Learning Algorithm

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AI Algorithms, Neural Networks Diagrams, Machine Learning Presentation (PPT Template)

## Naive Bayes - Supervised Learning Algorithm

### Slide Content

The slide introduces the Naive Bayes algorithm as a simple supervised classification method derived from Bayes theorem. It explains the formula P(class | x) which is the 'Posterior Probability' of a class given a predictor by multiplying the 'Likelihood' of the predictor given the class with the 'Class Prior Probability' and normalizing it by the 'Predictor Prior Probability'. Each term is elaborated: Likelihood signifies how often certain data features are associated with the class; Class Prior Probability indicates the general frequency of the class; and Predictor Prior Probability refers to the frequency of the predictor feature.

### Graphical Look

- Slide title is positioned at the top in large blue font
- A subtitle underneath in smaller font provides additional context
- A large rectangular light blue box on the right contains bulleted text explanations
- An arrowed flowchart on the left visually represents the algorithm's formula
- Four connected oval shapes in shades of blue and grey are used to symbolize concepts
- Each concept in the flowchart has an accompanying label in blue font
- Mathematical symbols and formula elements are clearly visible within the flowchart

The slide uses a professional and clean design with a balance of text and visuals. The color scheme is consistent with blue and grey tones, creating a cohesive and informative presentation.

### Use Cases

- To educate about Naive Bayes during a machine learning or data science course.
- In a business context, to explain the mathematical basis of a chosen algorithm for data classification.
- For technical presentations to stakeholders to illustrate the mechanisms behind predictive modeling.
- As part of an introductory workshop on statistics and probability in algorithm development.