Validate Algorithm Statistically: Questions & Answers

  • Context: Undergrad 
  • Thread starter Thread starter rickdatech
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SUMMARY

The discussion centers on the statistical validation of an algorithm with approximately 10 degrees of freedom. It is established that running the algorithm at least 30 times is necessary for statistical validation. Participants recommend researching statistical regression analysis as a key resource for understanding the validation process. The conversation highlights the importance of comprehending both the mathematical foundation and the rationale behind the algorithm for effective validation.

PREREQUISITES
  • Understanding of statistical validation techniques
  • Familiarity with degrees of freedom in statistical analysis
  • Basic knowledge of algorithm construction and evaluation
  • Experience with statistical regression analysis
NEXT STEPS
  • Research statistical regression analysis techniques
  • Explore methods for calculating degrees of freedom in algorithms
  • Learn about statistical validation processes and methodologies
  • Investigate resources on algorithm evaluation and performance metrics
USEFUL FOR

This discussion is beneficial for data scientists, algorithm developers, statisticians, and anyone involved in validating predictive models or algorithms.

rickdatech
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I understand a bit about math, but it's been over 30 years since I've been to college. I have a question about where to find information mostly. My prolbem is that I have an algorithm that guesses the results of a process. The process has as best I can figure about 10 degrees of freedom.

1) how many times would I need to run the algorithm against the process to statistically validate the algorithm?

2) where would I find an Internet source that discusses this type of statistical validation? none of the google terms I come up with work for me.

I understand I can prove it by showing the math of how the algorithm was constructed, but that only works if you know the "why" behind the algorithm.
 
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1. No less than 30.
2. Look up statistical regression analysis.
 

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