Fundamentals of Dask Transcripts
Chapter: Dask-ML
Lecture: Generating dataset with scikit-learn
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Welcome back and I'm so excited to jump into scikit-learn for machine learning with you
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now. So you may recall that scikit-learn is a powerful library for machine learning in Python which provides among many other things,
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tools for pre-processing, model training, evaluation and more. If your model and data fit on your computer,
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definitely use scikit- learn with no parallelism. Will soon see how to generalize your scikit-learn code using Dask-ML,
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to a parallel and distributed setting. So let's now see how you can train machine learning models in scikit- learn.
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So first we want to create a data set. You could import one. But "scikit-learn(sklearn)" has nice utility functions for creating datasets.
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So I've just executed this to use the 'make_classification' function to create a data set
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that has 100,000 data points And 10 features for each data point.
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Now you may note that we've unpacked the result of make classification into two variables X and Y. It's worth spending a minute talking about these.
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You may recall from your knowledge of machine learning that a machine learning challenge has feature
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variables which you input to your model and then output or target variables. That your model is trying to predict.
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What we're doing is unpacking the features into capital X by convention and the target into lower case 'y'. Also by convention,
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as we've written here, X is the set of input variables. And 'y' is the output or target variable.
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If we look at, let's say the first five entries or in this case, rows of X uh we should get something that's five by 10.
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So here we have five rows of 10 columns each. Similarly, what we hope to see when looking at the first five entries of of
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'y' a five binary elements. So zeros and ones. Because we're working in classification, we expect to see discrete outputs and the default here
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is binary, so we should say five zeroes and ones, as we do now in the next video,
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we're going to come back and build our very first machine learning model together.
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It will be a 'K-nearest neighbors Classification' for the data set that we've just generated