Python Tutorial: Hyperparameter tuning in python | Intro

Key Takeaways

welcome to the first lecture of hyper parameter tuning in Python I am Alex a data scientist from Sydney Australia so why study this course today algorithms are getting more and more complex and so the number of hyper parameters to choose from increases it becomes increasingly important to learn how to efficiently find optimal combinations as this search will likely take up a large portion of your time often it is quite easy to simply run scikit-learn functions on the default settings or perhaps code from a tutorial or book without really digging under the hood however what lies underneath is a vital importance to good model building you may be surprised by what you find this course will use a data set about credit card defaults it contains a number of variables related to the demographics and financial history of a group of people the target column shows whether or not they defaulted on their next loan payment it has already been pre processed and split ready to model note that at times we will take smaller samples to ensure that we can run the code you can find out more about it at the link in the slides to understand hyper parameters let's first start with parameters what are parameters parameters are components of the final model that are learned through the modeling process crucially you do not set these you cannot set these the algorithm discovers them through undertaking it steps to make this concrete consider a simple logistic regression model we create the estimator and fit to the data with default settings since the logistic regression model is a linear model we will get beta coefficients on our variables these are found in the co F property of our logistic regression object however if we print these out we can see it is a bit messy let us clean this up by creating a list of original variable names zipping this up with the coefficients and formatting into a neat data frame for easy viewing we can now sort the data frame and print the top three results for brevity do you recall setting pay zero to have a coefficient of 0.0 0.1 I don't the coefficients are parameters because we did not set them ourselves and we'll learn during the modeling process in our data the pay variables relate to how many months people have previously delayed their payments we can see that having a high number of months of delayed payments makes someone more likely to default next month to know what parameters and algorithm will produce you need to know a bit about the algorithm itself and how it works and consult the psychic learn documentation to see where the parameter is stored in the returned object the parameters are found in the documentation for that particular algorithm under the attribute section not the parameters section so what are the parameters entry based model that do not have linear coefficients the parameters of this model are in the nodes of the trees used to build the model such as what feature was split on and at what value to demonstrate that has firstly built a random forest estimator and fit to our data setting the max depth to be quite low only for visualization purposes then we can pull out a single tree found in the random forest estimator estimators attribute to visualize for simplicity we will choose show the image but you can explore visualizing this yourself using the mention packages here we see a graph of the nodes including the variables and values used in the splits we can see that the very first split was on the variable pay for and it sent samples left or right depending if they had a value above or below one for this variable do you remember setting this decision I certainly don't so how do we pull out the splits we saw here visually in a programmatic way let's say the left second from top node the tree we pulled out is a psychic learn tree object so we can find the variable it's split on by indexing into the dot feature attribute of this tree and matching up without X train columns to get the name the level used to split on is then found in the dot threshold attribute and we can print this out let's do some exercises to further explore the parameters