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Chapters 2 - 7 (Chapter 3 - Specify Business Problem (Companies generally…

- - - - Process of examining all descriptive statistics for features and their relationships with the target
    - - Cleaning data
      - Combining features
      - Splitting features into multiple features
    - - Choosing the correct algorithm
      - Choosing correct paramaters
    - - Evaluation of top models
      - Confusion matrix
  - - - Repeated segments long time series data
    - - Which direction something is headed
  - - - Implemented within another system
      - For specific purpose
    - - Open source
        
        Require Python and R
        
        Start with Auto-sklearn
      - Commercial
        
        Google APIs
        
        Amazon ML
  - - - Selecting which features to use and creating new ones automatically
      - Comparing and selecting a variety of models and tuning those models automatically
    - - Graceful handling of algorithm-specific needs
    - - System for coding data-scientists should be integrated into their process flow and easily undersetandable
      - Simple to implement the model into decision flow
      - Effect result can help reach predicted target (making diabetes patients watch a short video on how to deal with some issue)
    - - Should help understanding of problem context
      - Give evidence for analyst to stand their ground
    - - Be compatible with existing business systems
      - Integrate with other tools in intelligence ecosystem
      - Allow easy use of the resulting model through API
    - - Possible to see decisions made by the system
      - Enable evaluation of models beyond just their performance
        
        Which variables were most important to the neural networks
        
        Which were most important to logistic regression
    - - Work for all target types, data sizes, different time perspectives
    - - Become a subject matter expert to know how to make the probabilities and results useful - tp put things to action