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Or Rado Rado rat modelling Part 3 churning once you're happy with your models initial performance on
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your training dataset.
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The next step is to try and improve it like what we said in last lesson.
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How a car can be tuned for different styles of driving a model can be tuned for different types of data.
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Specifically your data usually this training will take place on a validation data split.
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However if you don't have access to a validation set due to how you've done your your training validation
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and test data split it can also happen on the training data on many models have different hyper parameters
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which can be adjusted.
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You can think of these as the same as dials on an oven.
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For example when you're learning to cook your favorite chicken dish that all that delicious sweet honey
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mustard dish.
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I'm getting hungry making these lectures.
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You notice that cooking it at 180 degrees for an hour meant it came out a little raw not ideal right.
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I want this chicken to be nice and crispy but at 200 degrees that's where we get the glory.
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That's 200 grains for an hour.
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My chicken came out just right.
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So like a little dial here Ryan.
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This is how you consume your oven.
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You can change it from 180 grains to be 200 degrees.
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If you'd lived 180 grains chicken doesn't turn out well you'd take it up 200 degrees.
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Chicken does turn out well.
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Depending on what kind of model you're using will depend on what kind of hyper properties you can chew.
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For example a random forest will allow you to adjust the number of trees in this one we've got three
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trees.
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So we've got five trees and we'll have a look at the random forest.
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This is a type of machine learning algorithm.
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We'll check this out in the future lesson and a no network will allow you to adjust the number of layers.
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Again we'll we'll look at a neural network in a future lesson just giving an example of different hyper
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parameters like the temperature of an oven.
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You can adjust on different kinds of algorithms.
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The main things for you to remember are machine learning models have hyper parameters you can adjust
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however depending on what model you're going to use the hyper parameters will be different.
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The goal of tuning hybrid parameters is to improve your model's performance and you should do model
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tuning on your training and or validation sets meaning you train a model using an initial set of hyper
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parameters.
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These might be the defaults like say for example you put your chicken ingredients in the oven you press
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chicken roast chicken on the oven and it goes for an hour on the ovens default settings and then you
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find it doesn't turn out how you want it to be you might adjust them as you go.
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This is exactly what you do with a machine learning model.
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You might try a machine learning model with an initial set of hyper parameters the default.
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And it turns out it does okay but not exactly how you want it.
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So you might try and improve it by adjusting some of the the hyper parameters up next we've got model
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comparison.
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Now this happens during your experimentation so you've trained multiple different models on the same
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dataset.
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We're going to look at how you might compare those and this is done on the test data.
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So let's do it.
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