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Supervised Learning in R: Regression

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

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4 Hours19 Videos65 Exercises31,619 Learners5300 XPData Scientist TrackMachine Learning Fundamentals TrackMachine Learning Scientist Track

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Course Description

From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions.

  1. 1

    What is Regression?


    In this chapter we introduce the concept of regression from a machine learning point of view. We will present the fundamental regression method: linear regression. We will show how to fit a linear regression model and to make predictions from the model.

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    Welcome and Introduction
    50 xp
    Identify the regression tasks
    50 xp
    Linear regression - the fundamental method
    50 xp
    Code a simple one-variable regression
    100 xp
    Examining a model
    100 xp
    Predicting once you fit a model
    50 xp
    Predicting from the unemployment model
    100 xp
    Multivariate linear regression (Part 1)
    100 xp
    Multivariate linear regression (Part 2)
    100 xp
    Wrapping up linear regression
    50 xp
  2. 2

    Training and Evaluating Regression Models

    Now that we have learned how to fit basic linear regression models, we will learn how to evaluate how well our models perform. We will review evaluating a model graphically, and look at two basic metrics for regression models. We will also learn how to train a model that will perform well in the wild, not just on training data. Although we will demonstrate these techniques using linear regression, all these concepts apply to models fit with any regression algorithm.

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  3. 3

    Issues to Consider

    Before moving on to more sophisticated regression techniques, we will look at some other modeling issues: modeling with categorical inputs, interactions between variables, and when you might consider transforming inputs and outputs before modeling. While more sophisticated regression techniques manage some of these issues automatically, it's important to be aware of them, in order to understand which methods best handle various issues -- and which issues you must still manage yourself.

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  4. 4

    Dealing with Non-Linear Responses

    Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity. This includes predicting probabilities and frequencies (values bounded between 0 and 1); predicting counts (nonnegative integer values, and associated rates); and responses that have a non-linear but additive relationship to the inputs. These algorithms are variations on the standard linear model.

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In the following tracks

Data ScientistMachine Learning FundamentalsMachine Learning Scientist


sumedhpanchadharSumedh PanchadharrichieRichie Cotton
John Mount Headshot

John Mount

Co-founder, Principal Consultant at Win-Vector, LLC

John is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. He is the author of several R packages, including the data treatment package vtreat. John is co-author of Practical Data Science with R and blogs at the Win-Vector Blog about data science and R programming. His interests include data science, statistics, R programming, and theoretical computer science.
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Nina Zumel Headshot

Nina Zumel

Co-founder, Principal Consultant at Win-Vector, LLC

Nina is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. She is co-author of the popular text Practical Data Science with R and occasionally blogs at the Win-Vector Blog on data science and R. Her technical interests include data science, statistics, statistical learning, and data visualization.
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