No free lunch

The No Free Lunch theorem is related to machine learning and it popularly states the limitation of any machine learning model. As per the theorem, there is no model that fits the best for every problem. So, one model that fits well for one problem in a domain may not hold good for another. So in practice ,whenever you are solving a problem, you need to try out different models and experiment with your dataset to choose the best one. This is especially true for supervised learning; you use the Evaluate Model module in ML Studio to assess the predictive accuracies of multiple models of varying complexity to find the best model. A model that works well could also be trained by multiple algorithms, for example, linear regression in ML Studio can be trained by Ordinary Least Square or Online Gradient Descent.