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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 Horas19 Videos65 Ejercicios
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Descripción del curso

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.
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  1. 1

    What is Regression?

    Gratuito

    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.

    Reproducir Capítulo Ahora
    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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En las siguientes pistas

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Científico de datos asociado en R

Ir a la pista

Fundamentos del machine learning en R

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Científico de Machine Learning con R

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Sets De Datos

BikesBlood PressureCricketHouse PricesIncomeMpgSoybeanUnemploymentSparrow

Colaboradores

Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Richie Cotton
Nina Zumel HeadshotNina Zumel

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

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