Eve: Building RESTful APIs with MongoDB and Flask Transcripts
Chapter: Your first Eve service
Lecture: Recap
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In this section we're going to make a model that predicts whether something is positive or negative.
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I'm going to use the XGBoost model. Make sure you have installed XGBoost. We're going to import that. All I'm going to do here is
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I'm going to say x is equal to this tfdf and our y is going to be equal to whether something is positive.
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So what's our tfdf? That is our data frame that we stuck onto the end of the other one. So let's look at x. x looks like this.
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It's a bunch of numbers in here. So this is basically how important 10 is in document 3.
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You can see that zone appeared in this document but a lot of these are zeros. This is sparse because not all of the reviews have all the
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columns. Okay what does y look like? Y is just a series whether something is positive or negative. And what am I going to do here? I'm going
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to use scikit-learn to split our data into a training set and a testing set. Why do we want a training set and testing set? Well we want to see how our
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model would perform on data that it hasn't seen before. So what we do is we hold out some subset of that, we train a model, and then with
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the subset we held out we evaluate our model. We already know what the true positive negative labels are but we see
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how well our model predicts those based on data that it hasn't seen, giving us some sort of feel of how it might perform in the real world.
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Okay so we've split up our data let's train our model. It's really easy to train a model you just say fit. So we're going to fit it with the x and
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the y. The x are the features the y is the label whether something was positive or negative. That takes a while because we've got a
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lot of columns but it looks like we did get a model that came out of that. And then let's evaluate it. One way to evaluate it is to use the score.
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We're going to pass in the testing data, the data that it hasn't seen, and we're going to pass in the real labels for that and this is going to
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give us back an accuracy. It looks like it got 78% right. Is 78% good? Well the answer to that is it depends.
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It might be good it might not be good. This is saying that basically four-fifths of the reviews that you classify as positive or negative
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are correct. Now if you have a situation where you're making a model that predicts maybe fraud, and fraud is not very common like you
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could imagine fraud might occur in like one in a thousand, well you could make a model that's highly accurate. You just predict not fraud.
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It's 99% accurate. So accuracy in and of itself might not be a sufficient metric to determine whether something's good, but
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it's good to give us a baseline. This is better than flipping a coin. It's 80% accurate.