Machine Learning Model Evaluation Methods
Introduction In both business and research, as we are getting more and more dependent on machine learning for solving strategic importance problems, real value must be added to organizations and business through the predictions that a machine learning model gives. While sometimes, we might have enough data that we use to train a model but it might so happen that it may not,in the future, be able to generalize to new data for which we wish to predict something. Evaluating the model based on the data that we used for training makes little sense in assessing whether the model is a proper fit for the case. The reason being, the machine already learns from, what we call, the training data and when we try to predict for the same, then the machine remembers that correctly and by implication, predicts fairly well. This is called overfitting. Our intention must be to see if the model generalizes well, so that it is able to predict correctly for new data, whether it actually works and how tr...