Data and supervised machine learning

- [Instructor] Now we're going to do a high level review…of supervised machine learning,…which essentially is taking historical data…to build a model which we then score on new data.…So let's take a closer look.…In our historical data…we have to have an established outcome.…So if we're talking about loans,…they paid or perhaps they defaulted on their mortgage.…We need that end result to be known.…Then we need predictors.…There can be numerous predictors.…

Hopefully we have dozens or hundreds of them.…Examples could include things like this.…Whether or not the loan in question…is their primary mortgage…or maybe it's a second mortgage.…Or what percentage of their income…is being paid to housing.…And this can help us predict that end result.…So in order to do it.…In order to map those predictors…to the end result,…we need a modeling algorithm.…And modelers are experts in this.…And there's going to be many of them…that they might draw upon.…They have names like Decision Tree,…Support Vector Machine, Logistic Regression,…