When designing a machine learning model, what in your opinion is more important, model performance or model accuracy, and why?
Machine Learning as we know it is revolutionizing the way we buy, the way we live, and the way we work. Companies everywhere use machine learning models to improve the way they do business. It helps companies make more precise business decisions based on the data they have. When developing a machine learning model, one has to rely on data that can be used to develop new products, and provide insights into business operations and decision making processes.
Interviewers ask this question because they are interested in a few different things from you. The first thing is your opinion on the importance of model performance and model accuracy. Your opinion should not heavily discount one over the other since they're interested to hear if you have worked with both, and why you have a preference over one or the other. Give an example and explanations of why you think one is better than the other, and detail the features, processes or techniques that you feel work better for you or the company. You could mention that model accuracy is used for identifying relationships and patterns between variables in a dataset. Model performance can also be mentioned as calculating current values based on a prediction and comparing the model's performance.
"In my opinion, I believe that model performance and model accuracy have an equally important role to play in Machine Learning. When I work with model accuracy, I understand that the better the data is the better the outcome of the results will be. This data provides better predictions and insights that deliver more business value, and by optimizing model accuracy, it mitigates cost as well. I find this to be important at many levels of the organization because while there is a point of diminishing returns, the value of more accurate models corresponds to profit increases for the organization. When I use the Machine Learning model performance method, I start with a baseline model, and then I determine if the model skill is relative, and then assign a score to it. This method, in my opinion, is slightly better, but not by much. The biggest reasons are; mean outcome values for a regression and classification problem, and better input and output for forecasting."
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