Varying Outcomes with Identical Algorithms

Requirements Clarification & Assessment

To effectively address the interview question, we need to clarify several aspects:

1. Definition of "Same Dataset":

   • Does "same dataset" imply using identical training and testing datasets, or just the same training dataset?
   • Are there any variations in data preprocessing, such as shuffling or augmentation?

2. Definition of "Same Algorithm":

   • Does "same algorithm" refer to using the same version and configuration of the algorithm?
   • Are there any differences in hyperparameter tuning or initializations?

3. Success Rate Context:

   • What metric is used to measure success rates (e.g., accuracy, precision, recall)?
   • Are success rates assessed on training data, testing data, or both?

4. Operational Environment:

   • Are there any variations in the computational environment, such as hardware or software versions?

By clarifying these points, we can better understand the nuances of the question and provide a structured and comprehensive answer.