‫Or Rado Rado Rak modelling part three tuning.
‫Once you're happy with your model's initial performance on your training data set, the next step is
‫to try and improve it.
‫Like what we said in the last lesson how a car can be tuned for different styles of driving.
‫A model can be tuned for different types of data, specifically your data.
‫Usually this training will take place on a validation data split.
‫However, if you don't have access to a validation set due to how you've done your your training, validation
‫and test data split, it can also happen on the training data.
‫Many models have different type of parameters which can be adjusted.
‫You can think of these as the same as Dols on an oven.
‫For example, when you're learning to cook your favorite chicken dish that oh, that delicious, sweet
‫honey mustard dish.
‫I'm getting hungry making these lectures.
‫You notice that cooking it at 180 degrees for an hour meant it came out a little raw.
‫Not ideal, right?
‫We want this chicken to be nice and crispy, but at 200 degrees, that's where we get the glory.
‫That's 200 degrees for an hour.
‫The chicken came out just right.
‫So, like a little dial here.
‫This is how you can tune your oven.
‫You can change it from 180 degrees to b 200 degrees.
‫If you leave it on 180 degrees, chicken doesn't turn out well.
‫If you take it up to 200 degrees, chicken does turn out well.
‫Depending on what kind of model you're using, will depend on what kind of hyper parameters you can
‫tune.
‫For example, a random forest will allow you to adjust the number of trees.
‫In this one, we've got three trees.
‫And this one we've got five trees.
‫Don't worry.
‫We'll have a look at the random forest.
‫This is the type of machine learning algorithm we'll we'll check this out in a future lesson.
‫And a neural network will allow you to adjust the number of layers.
‫Again, we'll look at a neural network in a future lesson.
‫Just giving an example of different hyper parameters like the temperature on an oven.
‫You can adjust on different kinds of algorithms.
‫The main things for you to remember are machine learning models have hyper parameters.
‫You can adjust.
‫However, depending on what model you're going to use, the hyper parameters will be different.
‫The goal of tuning hybrid parameters is to improve your model's performance, and you should do model
‫tuning on your training and or validation sets, meaning you train a model using an initial set of hyper
‫parameters.
‫These might be the defaults like say for example, you put your chicken ingredients in the oven, you
‫press chicken, roast chicken on the oven, and it goes for an hour on the oven's default settings.
‫And then you find it doesn't turn out how you want it to be.
‫You might adjust some as you go.
‫This is exactly what you'll do with a machine learning model.
‫You might try a machine learning model with an initial set of hyper parameters, the default, and it
‫turns out it does okay, but not exactly how you want it.
‫So you might try and improve it by adjusting some of the the hyper parameters.
‫Up next, we've got model comparison.
‫Now, this happens during your experimentation.
‫So you've trained multiple different models on the same data set.
‫We're going to look at how you might compare those.
‫And this is done on the test data.
‫So let's do it.