Machine Learning Model Accuracy

What does Machine Learning Model Accuracy mean?

Machine learning model accuracy is the measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training, data. The better a model can generalize to ‘unseen’ data, the better predictions and insights it produces that deliver more business value.

Why is Model Accuracy important?

Companies use machine learning models to make practical business decisions, and more accurate model outcomes result in better decisions. The cost of errors can be huge, but optimizing model accuracy mitigates that cost. While there is a point of diminishing returns when the value of developing a more accurate model wouldn’t result in a corresponding profit increase, in some cases – like a falsely positive cancer diagnosis, which would cost the hospital and the patient both time and undue stress – it’s worth the extra effort.

DataRobot + Model Accuracy

The DataRobot automated machine learning platform uses top open-source algorithms to enable its users to develop extremely accurate, highly interpretable models with the click of a button. It thoroughly tests the accuracy of its models with 5-fold cross-validation and provides insights to help reduce the likelihood of factors like target leakage that might be inhibiting model accuracy and therefore negatively impacting the decision-making process.