How to Tell If Your Machine Learning Model Is Accurate

Accuracy is crucial for success in machine learning, but how do developers measure it? Several mathematical testing methods can reveal how accurate a machine learning model is and what types of predictions it is struggling with. The foundation of machine learning accuracy is the confusion matrix. The confusion matrix is used to compare the predictions of a machine-learning model with reality. True positives and true negatives are predictions that match reality, while false negatives and false positives are incorrect predictions.