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  3. Supervised Learning in R: Classification
  • 1

    k-Nearest Neighbors (kNN)

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    As the kNN algorithm literally "learns by example" it is a case in point for starting to understand supervised machine learning. This chapter will introduce classification while working through the application of kNN to self-driving vehicle road sign recognition.

  • 2

    Naive Bayes

    Naive Bayes uses principles from the field of statistics to make predictions. This chapter will introduce the basics of Bayesian methods while exploring how to apply these techniques to iPhone-like destination suggestions.

  • 3

    Logistic Regression

    Logistic regression involves fitting a curve to numeric data to make predictions about binary events. Arguably one of the most widely used machine learning methods, this chapter will provide an overview of the technique while illustrating how to apply it to fundraising data.

  • 4

    Classification Trees

    Classification trees use flowchart-like structures to make decisions. Because humans can readily understand these tree structures, classification trees are useful when transparency is needed, such as in loan approval. We'll use the Lending Club dataset to simulate this scenario.

Initializing

Ubung

A simple Naive Bayes location model

The previous exercises showed that the probability that Brett is at work or at home at 9am is highly dependent on whether it is the weekend or a weekday.

To see this finding in action, use the where9am data frame to build a Naive Bayes model on the same data.

You can then use this model to predict the future: where does the model think that Brett will be at 9am on Thursday and at 9am on Saturday?

The data frame where9am is available in your workspace. This dataset contains information about Brett's location at 9am on different days.

Anweisungen

100 XP
  • Load the naivebayes package.
  • Use naive_bayes() with a formula like y ~ x to build a model of location as a function of daytype.
  • Forecast the Thursday 9am location using predict() with the thursday9am object as the newdata argument.
  • Do the same for predicting the saturday9am location.