regularization you ask for model with as few predictors as possible this is important for the model' robustnesssimple solutions tend to hold true in more situations regularization aims to keep the variance between the coefficients of the predictors as small as possible overlapping variance between predictors makes it hard to make out the actual impact of each predictor keeping their variance from overlapping will increase interpretability to keep it simpleregularization is mainly used to stop model from using too many features and thus prevent over-fitting validation is extremely important because it determines whether your model works in real-life conditions to put it bluntlyit' whether your model is worth dime even soevery now and then people send in papers to respected scientific journals (and sometimes even succeed at publishing themwith faulty validation the result of this is they get rejected or need to retract the paper because everything is wrong situations like this are bad for your mental health so always keep this in mindtest your models on data the constructed model has never seen and make sure this data is true representation of what it would encounter when applied on fresh observations by other people for classification modelsinstruments like the confusion matrix (introduced in but thoroughly explained later in this are goldenembrace them once you've constructed good modelyou can (optionallyuse it to predict the future predicting new observations if you've implemented the first three steps successfullyyou now have performant model that generalizes to unseen data the process of applying your model to new data is called model scoring in factmodel scoring is something you implicitly did during validationonly now you don' know the correct outcome by now you should trust your model enough to use it for real model scoring involves two steps firstyou prepare data set that has features exactly as defined by your model this boils down to repeating the data preparation you did in step one of the modeling process but for new data set then you apply the model on this new data setand this results in prediction now let' look at the different types of machine learning techniquesa different problem requires different approach types of machine learning broadly speakingwe can divide the different approaches to machine learning by the amount of human effort that' required to coordinate them and how they use labeled data--data with category or real-value number assigned to it that represents the outcome of previous observations supervised learning techniques attempt to discern results and learn by trying to find patterns in labeled data set human interaction is required to label the data unsupervised learning techniques don' rely on labeled data and attempt to find patterns in data set without human interaction