supervised learning now in contrastsupervised learning is case where we have set of answers that the model can learn from we give it set of training datathat the model learns from it can then infer relationships between the features and the categories that we wantand apply that to unseen new values and predict information about them going back to our earlier examplewhere we were trying to predict car prices based on the attributes of those cars that' an example where we are training our model using actual answers so have set of known cars and their actual prices that they sold for train the model on that set of complete answersand then can create model that ' able to use to predict the prices of new cars that haven' seen before that' an example of supervised learningwhere you're giving it set of answers to learn from you've already assigned categories or some organizing criteria to set of dataand your algorithm then uses that criteria to build model from which it can predict new values evaluating supervised learning so how do you evaluate supervised learningwellthe beautiful thing about supervised learning is that we can use trick called train/test the idea here is to split our observational data that want my model to learn from into two groupsa training set and testing set so when train/build my model based on the data that havei only do that with part of my data that ' calling my training setand reserve another part of my data that ' going to use for testing purposes can build my model using subset of my data for training dataand then ' in position to evaluate the model that comes out of thatand see if it can successfully predict the correct answers for my testing data so you see what did therei have set of data where already have the answers that can train my model frombut ' going to withhold portion of that data and actually use that to test my model that was generated using the training setthat it gives me very concrete way to test how good my model is on unseen data because actually have bit of data that set aside that can test it with you can then measure quantitatively how well it did using -squared or some other metriclike root-mean-square errorfor example you can use that to test one model versus another and see what the best model is for given problem you can tune the parameters of that model and use train/test to maximize the accuracy of that model on your testing data so this is great way to prevent overfitting