What do you do if your algorithmic solution is being questioned for accuracy and correctness?

If the algorithm accuracy and correctness is being doubted, I generally follow the below steps - 1. Formulate the mathematical equations and check for numerical consideration - for ex a float will not have the same range/ precision as double. 2. Check the algorithm against standard test inputs if available. 3. Check whether the input data is in conformance with the expectation. 4. Analyse the problem with a systems perspective - for ex, when a data is being represented by a fit, the error distribution should have zero bias.

[1]First, check the logic to be implemented is reflected in code. [2]Remove the outliers from the input [3] Define proper metrics for accuracy, identify parameters that influence accuracy. [4]test with different use cases

Always use a test set and a validation set. The validation set will be used as a reference during training and your test set affirms the evaluation metric whether that is accuracy, precision or recall. Use statistics, simulate whether the result is significant. Use these evidences to support the performance of the algorithm

here's what I can do: 1.Verify my Solution 2.Review the Problem Statement 3.Seek Feedback 4.Debugging 5.Compare with Accepted Solutions 6.Communicate Effectively 7.Stay Calm and Focused

It’s great that your algorithm is being criticized. That’s how you can improve it. Try to improve it based on the feedback you got

The algorithms can be validated in multiple ways and using other evaluation metrics can be brought into pic to validate the algorithm. Checking and hypothesis and reviewing it with the team members might bring in a fresh perspective. Extrapolating to real world data and monitoring the algorithm to see if there is quicker data drift or not would be helpful.

Scrutinize the Process: After verifying problematic results, go deeper by reviewing your algorithm's logic. Question Every Step: Is each part necessary and correct? Do steps flow in the right order? Walkthrough Mentality: Try explaining the algorithm to someone else, or 'step through' the process manually. Spotting where your explanation falters often highlights the problem area.

Examine the logic and reasoning employed by the algorithm to identify any potential flaws or inconsistencies. Trace the algorithm's execution step by step to understand how inputs are processed and transformed into outputs. Look for areas where the logic may break down or produce unexpected results. Example: Review the logic of a recommendation algorithm used by a streaming service to suggest movies to users. Examine how the algorithm selects recommendations based on user preferences and viewing history. Identify potential gaps in logic, such as overlooking niche interests or failing to account for recent viewing trends, and refine the algorithm's decision-making process accordingly.

To review the logic of an algorithm, first, ensure a clear understanding of the problem and its requirements. Break down the algorithm into manageable steps and verify any assumptions. Follow the flow of the algorithm, ensuring each step logically leads to the next. Verify correctness by hand-executing or debugging the algorithm, considering edge cases and optimizations along the way. Finally, validate results with diverse test cases to confirm accuracy. This systematic approach helps ensure the algorithm effectively addresses the problem it intends to solve.

Checking assumptions ensures alignment between the problem statement and the solution approach. It helps confirm that underlying conditions and constraints are accurately interpreted and addressed in the algorithm. By scrutinizing assumptions, one can detect potential mismatches or inaccuracies, refining the solution to better suit the problem's requirements. Additionally, verifying assumptions fosters a thorough understanding of the problem space, enabling adjustments if initial interpretations were incorrect. This process enhances the algorithm's accuracy and correctness by ensuring it operates within the intended scope and context.

When checking assumptions in an algorithm, you're basically looking at the things it takes for granted. Make sure these are sensible and match the real world. Ask yourself if they're believable and backed up by facts or evidence. Also, think about any special cases that might not fit the usual assumptions. By doing this, you can make sure the algorithm's logic is solid and its results are trustworthy.

Question Your Assumptions: Did something change that makes your initial assumptions invalid? Data Evolution: Has new data or updated characteristics rendered your assumptions incorrect? Shifting Environment: Have external factors changed the conditions for which the algorithm was designed?

Review the assumptions underlying the algorithm to determine if they accurately reflect the problem domain and constraints. Question whether any simplifications or generalizations made during the algorithm's design could be leading to inaccuracies. For instance, Consider a route optimization algorithm for a delivery service that assumes constant traffic conditions throughout the day. Check this assumption by analyzing historical traffic data to determine if traffic patterns vary significantly during different times of the day or week. Adjust the algorithm to account for fluctuations in traffic congestion.

To check assumptions in algorithms, first identify any stated or implied assumptions. Then, validate them with real-world data or conditions, considering edge cases. Update documentation to reflect any revised understanding.

Don't Go It Alone: When facing algorithm issues, get help from your peers. Fresh Eyes: Colleagues often spot things you might have missed. Diverse Expertise: Tap into skills or knowledge you may not possess yourself. Learning Opportunity: Collaborating on problems builds a stronger team and improves your own skills.

Seek feedback and input from peers, colleagues, or subject matter experts to gain alternative perspectives on the algorithm's accuracy and correctness. Encourage constructive criticism and open dialogue to uncover potential blind spots or overlooked issues. Collaboratively review the algorithm's design, implementation, and results to identify areas for improvement. For instance, organize a peer review session where team members can collectively analyze the algorithm's performance and offer insights based on their expertise.

To engage peers in checking assumptions in algorithms: 1. Present the algorithm and assumptions clearly. 2. Seek feedback on assumptions and validation approach. 3. Collaboratively test assumptions. 4. Peer-review documentation and results. 5. Discuss findings in forums or meetings. 6. Review documentation for accuracy. 7. Acknowledge peer contributions.

- Engage with people from various domains of expertise - If confidentiality allows, engage with people outside your organization / research team

Engaging with peers offers valuable perspectives and insights that can uncover blind spots or errors in the algorithmic solution. Peer review encourages constructive criticism and collaborative problem-solving, allowing for diverse viewpoints to challenge assumptions and verify correctness. Through discussions and feedback, potential flaws or overlooked details can be identified and addressed, leading to a more robust and accurate solution. Moreover, peer engagement fosters a supportive environment for learning and growth, enhancing individual understanding and collective problem-solving skills.

Documentation is Key: When problems with your algorithm arise, having clear and updated documentation is invaluable. Explain Your Changes: Document modifications and the reasons behind them. Aid Future Understanding: Well-written documentation makes it easier for others (and future-you!) to troubleshoot the algorithm. A Record of Thought: Good documentation can even help defend your algorithm's logic if needed.

Updating algorithm documentation involves revising assumptions, clarifying steps, documenting optimizations, providing examples, updating references, conducting a thorough review, and publishing for user access. This ensures accuracy and clarity, aiding users in effectively understanding and utilizing the algorithm.

- Don't hesitate to use available tools to help you with documentation (e.g. LLMs) - It's always a good idea to ask for a review on your documentation (at least the most critical parts of it) to make sure it is clear and self-explanatory

Every challenge to an algorithm presents a chance for improvement. Feedback helps refine algorithms, making them more robust and accurate. Mistakes offer valuable lessons, revealing weaknesses and prompting adjustments to prevent similar errors in the future. Correct results reinforce confidence but also emphasize the importance of clear communication to convey the algorithm's correctness effectively. Continuous learning and adaptation are essential for algorithmic proficiency, ensuring algorithms evolve with the changing landscape of problems they aim to solve.