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Now we've gone through Step 1 into problem definition and data.
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It's time for step 3 evaluation every machine learning problem you come across.
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We'll have the similar goal of finding insights in data to predict the future in some way an evaluation
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metric is a measure of how well a machine learning algorithm predicts the future.
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And in this step the question you'll want to answer is what defines success for us.
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For example if your problem is to use patient medical records to classify whether someone has heart
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disease or not you might start by saying for this project to be valuable we need a machine learning
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model with over ninety nine percent accuracy because predicting whether or not a patient has heart disease
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is an important task.
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So you'll want a highly accurate model and as you could imagine there are different evaluation metrics
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for different problems for classification or predicting whether something is one thing or another.
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Accuracy precision and recall a common for regression or predicting a number such as how much a car
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will sell for.
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You'll probably want to minimize how different the number your model predicts to the actual sale price
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for this mean absolute error and main square area are common options or for recommendation problems.
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You may have thousands of different products to recommend to someone but in reality you only care about
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the top 10 recommendations and how well they align to a customer's potential interest.
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To measure this you could use precision at K where in our case k is 10 sitting down and thinking about
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an evaluation metric.
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At the start of a project ensures everyone on it has a similar goal to work towards but it's important
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to remember these don't have to be exact either as you find out about more about the data you might
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find the evaluation metric changes as the project goes on for now don't worry if you're not too sure
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about each of these we'll see different examples of these.
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As we build projects as an example of an evaluation metric being used in practice we had a project where
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we wanted to use the text from car insurance claims to predict who caused the accident the person submitting
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the claim or the other person involved the car insurance company we partnered with wanted at least a
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95 percent accurate model to consider the project worth continuing.
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This meant the model I was building had to be able to read a car insurance claim and predict with 95
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percent accuracy who caused the accident.
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This meant it was only allowed to get it wrong 1 out of 20 claims.
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Again we'll learn more about evaluation metrics when we get hands on for different projects.
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But the thing to remember is as you go you'll start to define a problem like in Step 1 then you'll start
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to remember all this is a classification problem I should use accuracy as my evaluation metric to get
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an idea of how my model is doing before we go on to Step Four have a think about different things you
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measure everyday.
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How do you measure them.
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Are there different kinds of measurements for different tasks.
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