machine learning training your model with the right predictors in place and modeling technique in mindyou can progress to model training in this phase you present to your model data from which it can learn the most common modeling techniques have industry-ready implementations in almost every programming languageincluding python these enable you to train your models by executing few lines of code for more state-of-the art data science techniquesyou'll probably end up doing heavy mathematical calculations and implementing them with modern computer science techniques once model is trainedit' time to test whether it can be extrapolated to realitymodel validation validating model data science has many modeling techniquesand the question is which one is the right one to use good model has two propertiesit has good predictive power and it generalizes well to data it hasn' seen to achieve this you define an error measure (how wrong the model isand validation strategy two common error measures in machine learning are the classification error rate for classification problems and the mean squared error for regression problems the classification error rate is the percentage of observations in the test data set that your model mislabeledlower is better the mean squared error measures how big the average error of your prediction is squaring the average error has two consequencesyou can' cancel out wrong prediction in one direction with faulty prediction in the other direction for exampleoverestimating future turnover for next month by , doesn' cancel out underestimating it by , for the following month as second consequence of squaringbigger errors get even more weight than they otherwise would small errors remain small or can even shrink (if < )whereas big errors are enlarged and will definitely draw your attention many validation strategies existincluding the following common onesdividing your data into training set with xof the observations and keeping the rest as holdout data set ( data set that' never used for model creation)--this is the most common technique -folds cross validation--this strategy divides the data set into parts and uses each part one time as test data set while using the others as training data set this has the advantage that you use all the data available in the data set leave- out --this approach is the same as -folds but with = you always leave one observation out and train on the rest of the data this is used only on small data setsso it' more valuable to people evaluating laboratory experiments than to big data analysts another popular term in machine learning is regularization when applying regularizationyou incur penalty for every extra variable used to construct the model with