R Tutorial: Recap of machine learning basics

Key Takeaways

good job you now have a solid understanding of the differences between model parameters and hyper parameters but Before we jump into actively tuning the hyper parameters we will briefly recap the basics of machine learning in art we will use the carrot package first because it automatically performs a basic hyper parameter tuning for you with every training run but before we look into this automatic high parameter tuning in carrot we need to prepare our data for training first we will divide our data into training and test sets carrot makes the step easy with the create data partition function let us give a class label vector as input for stratified partitioning of the data this is important because we want to have a roughly equal ratio of classes in our training and test set with the argument P we tell the function what proportion of the data should go into the training set here 70% the index that will be created can then be used for sub setting the original data set how much of the data you want to keep for training can be part of the optimization process there are really no strict rules on how to split the data but you want to make sure that you have enough training power and that you have a representative tested with a small data set such as this 70% is a common number but you will also often see 80 or 90% training data here I will not go into additional steps of the machine learning workflow like feature engineering pre-processing normalization balancing classes etc just keep in mind that in a real world scenario you would at least want to think about incorporating these steps into your workflow our validations team is defined in the train control function we will do five times three repeated cross-validation which means repeating three fold cross validation five times this scheme is then given as an argument in the train function in carrot we can train machine learning models with a large number of different algorithms we define this with the argument method in the train function here we will train a random forest model which is abbreviated our F train also wants to know which data in which feature to use our data set is the training set that we created before the features are given with a formula the class or response variable here diagnosis is written before and features after the tilly for features we write in dot here which indicates that we want to use all remaining columns as features in our model in addition I want to know how long my model took to Train for this I am using the tick-tock package which will return the run time between tick and tock as we can see our model took about 1.4 seconds to Train here is the random forest model we just trained in the output we can already see hyper parameter tuning and action as carrot performs it automatically with every option for the hyper parameter M try you will learn more about that in the next lesson what's important to note here is that carrot compares different a parameter on the training and validation data only do not be tempted to measure your model performance on the test data during hyper parameter tuning as that would give you an overly optimistic and biased performance evaluation now it's your turn