1 / 20

Know Thy Neighbor: An Introduction to Scikit-learn and K-NN

Know Thy Neighbor: An Introduction to Scikit-learn and K-NN. Portia Burton Portland Data Science Group March 25, 2014. What We will Cover Today. 1. Define What is Machine Learning 2. Go Over Scikit-learn 3. Explain k-Nearest Neighbor 4. Demo of Scikit-learn and k-Nearest Neighbor .

vanida
Télécharger la présentation

Know Thy Neighbor: An Introduction to Scikit-learn and K-NN

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Know Thy Neighbor: An Introduction to Scikit-learn and K-NN Portia Burton Portland Data Science Group March 25, 2014

  2. What We will Cover Today 1. Define What is Machine Learning 2. Go Over Scikit-learn 3. Explain k-Nearest Neighbor 4. Demo of Scikit-learn and k-Nearest Neighbor

  3. What is Machine Learning • The art of creating a predictive models • Uses input to make predictions • Enabling computers to pattern match data

  4. Scikit-Learn

  5. What is scikit-learn? • Python machine learning package • Built on NumPy, SciPy, and matplotlib

  6. **

  7. k-NN • k Nearest Neighbor algorithm • The simplest machine learning algorithm • K being the constant

  8. Basic Information about KNN • It is a lazy algorithm : doesn’t generalize the training data until approached with a new data point

  9. Supervised vs. Unsupervised Learning

  10. Supervised Learning When your samples are labeled

  11. Example: Spam Filters

  12. Unsupervised Learning The given instances are not labeled, and the categories are determined independently

  13. How k-NN works

  14. How k-NN works ?

  15. What can KNN be used for • Clustering • Regression

  16. Downsides of KNN • Since there is minimum training there is a high cost in testing new data • Correlation is falsely high (data points can be given too much weight)

  17. Alternatives to kNN KDTree BallTree

  18. References: http://www.solver.com/xlminer/help/k-nearest-neighbors-prediction-example http://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/ http://scikit-learn.org/stable/modules/neighbors.html http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html http://stackoverflow.com/questions/1832076/what-is-the-difference-between-supervised-learning-and-unsupervised-learning http://stackoverflow.com/questions/2620343/what-is-machine-learning

More Related