Lecture on Machine Learning Models

Introduction

• Importance of prior videos: foundation for understanding machine learning and its usefulness
• Key term: model

What is a Model?

• A model is a representation of reality
• Data set: a large collection of data representing reality
  • More data = closer to reality
  • Sometimes a data set alone is not enough

Splitting Data Sets

• Representation of reality by splitting data into sections
• Example: Predicting diabetes
  • Data set includes health information, age, family history, etc.
  • Each piece of data represents an instance of reality

Simplified Data Representation

• Simplification for understanding purpose
  • E.g., Binary data (yes/no, male/female)
• Creating a table with this simplified data
  • Columns: Attributes like sex, age, family history
  • Extra Column: Whether they had diabetes

Using Historical Data

• Historical data helps in making accurate models
• Modeling reality using if statements
  • Example: If sex is male, age < 50, history = true, then diabetes = false
• Simplifies understanding but not practical for large data sets

Predictive Data Analytics vs. Machine Learning

• For data analytics: Fine to use simple if statements/model
• For machine learning: Deals with incomplete data representation
  • Predict best model for unknown data sections
  • The model is the choice the algorithm makes

Possibilities and Model Selection

• How many models are possible?
  • Example situation: four models
  • Consider only rows without data
  • Different combinations (e.g., both true, both false)
• Machine learning algorithm chooses the best possible model
  • Based on other data

Conclusion

• Machine learning models: most likely representations of reality from incomplete data
• Video aim: Better understanding of what a model is in machine learning

End Note: The importance of understanding models and how machine learning selects the best model.