Writing Question
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Get it written →- Watchhttps://www.youtube.com/watch?v=JnnaDNNb380Links to an external site.
for a very basic overview of supervised vs. unsupervised learning as well as an intro to k-means clustering. Disregard the last part about autoencoders–that’s beyond the scope of this course. - Open Orange with a blank canvas/project.
- Drop a ‘datasets’ widget onto the canvas.
- Configure it to pull the Iris dataset. (find it, then double-click on it)
- Connect a data table widget to ensure that you’re getting the correct data.
- From the data table widget, connect a k-means widget (blue, unsupervised learning).
- Configure it for three (3) fixed clusters.
- Connect a scatter plot widget to visualize the output, selecting the “Color” option to use the Cluster.
- Leaving that window open, go back to the k-means widget config and change the number of clusters–2, 4, etc. to see what happens.
- Your submission will be:
- a minimum of 100 words explaining the basic functionality of k-means, including your observations from changing the ‘k’ parameter (the number of target clusters)
- For a good (and corny but entertaining) explanation of k-means, watchhttps://youtu.be/4b5d3muPQmALinks to an external site.
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- For a good (and corny but entertaining) explanation of k-means, watchhttps://youtu.be/4b5d3muPQmALinks to an external site.
- a screenshot of your scatter plot with 3 clusters (k=3)
- a single Word document with your written explanation and screenshot
- a minimum of 100 words explaining the basic functionality of k-means, including your observations from changing the ‘k’ parameter (the number of target clusters)
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