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 can produce, which in turn 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. There is, of course, a point of diminishing returns when the value of developing a more accurate model won’t result in a corresponding profit increase, but often it is beneficial across the board. A false positive cancer diagnosis, for example, costs both the hospital and the patient. The benefits of improving model accuracy help avoid considerable time, money, and undue stress.
DataRobot + Model Accuracy
The DataRobot AI Cloud 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 reduce the likelihood of factors such as target leakage which may inhibit model accuracy and therefore negatively impacting the decision-making process.
- Blog: How to Understand a DataRobot Model: Comparing Models for Accuracy [Part 2]
- Blog: How to Understand a DataRobot Model: Drilling Down into Model Accuracy [Part 3]
- End-to-End AI: The Complete Guide to DataRobot’s Enterprise AI Platform
- White Paper: Data Preparation for Automated Machine Learning