Submit your responses to the three (3) numbered questions below as a single Word document–along with pasted screenshots of the output from the KNN notebook.
Submit your responses to the three (3) numbered questions below as a single Word document–along with pasted screenshots of the output from the KNN notebook.
Explain supervised learning in your own words. Put yourself in the shoes of someone without any clue what it is–and help them understand it. Minimum 75 words.
Explain the k-nearest-neighbor algorithm, and discuss its pros and cons as a supervised learning algorithm. Does it scale well to datasets with a large number of features? Minimum 75 words.
The original article links for this assignment are posted in Canvas, but I have edited them and produced PDFs for easier access:
Intro to Supervised Learning: A Brief Introduction to Supervised Learning – Medium Article.pdfDownload A Brief Introduction to Supervised Learning – Medium Article.pDownload A Brief Introduction to Supervised Learning – Medium Article.pdfDownload A Brief Introduction to Supervised Learning – Medium Article.p
Article on K-Nearest-Neighbor: KNN visualization in just 13 lines of code.pdf Download KNN visualization in just 13 lines of code.pdfDownload KNN visualization in just 13 lines of code.pdf
I’ve put together the code from the KNN article into a notebook. Download it and experiment with it by varying the value of the hyperparameter k.
KNN_intro_and_experimentation.ipynb Download KNN_intro_and_experimentation.ipynb Download KNN_intro_and_experimentation.ipynb(note that the data is part of the scikit-learn library, so nothing to download other than the notebook)
3. What do you think the optimal value of k might be for the iris dataset (in this case only using the sepal width and length as the two features)?
Submit a single Word document with answers to the three (3) questions posed as well as at least two (2) pasted screenshots showing output using two different values of k.
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