Codeer een eenvoudige regressie met één variabele

In deze eerste codeeropdracht maak je een formule voor een model met één variabele en pas je vervolgens een lineair model op de data. Je krijgt de percentages mannelijke en vrouwelijke werkloosheid in de Verenigde Staten over meerdere jaren (Bron).

De taak is om het percentage vrouwelijke werkloosheid te voorspellen op basis van het waargenomen percentage mannelijke werkloosheid. De uitkomst is female_unemployment, en de input is male_unemployment.

Het teken van de variabelecoëfficiënt vertelt je of de uitkomst toeneemt (+) of afneemt (-) naarmate de variabele toeneemt.

Onthoud dat de aanroep voor lm() (docs) is:

lm(formula, data = ___)

Het data frame unemployment is al voor je geladen.

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

Cursus bekijken

Oefeninstructies

Definieer een formule die female_unemployment uitdrukt als functie van male_unemployment. Ken de formule toe aan de variabele fmla en print deze.
Gebruik daarna lm() en fmla om een lineair model te fitten dat vrouwelijke werkloosheid voorspelt op basis van mannelijke werkloosheid met de gegevensset unemployment.
Print het model. Is de coëfficiënt voor mannelijke werkloosheid in lijn met wat je zou verwachten? Neemt de vrouwelijke werkloosheid toe als de mannelijke werkloosheid toeneemt?

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# unemployment is available
summary(unemployment)

# Define a formula to express female_unemployment as a function of male_unemployment
fmla <- ___

# Print it
___

# Use the formula to fit a model: unemployment_model
unemployment_model <- ___

# Print it
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Supervised Learning in R: Regressie

SkillTag.level.intermediateSkillTag.label

4.6+

Begin de cursus gratis

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.

Exercise 1: Welkom en introductie Exercise 2: Herken de regression-taken Exercise 3: Lineaire regressie - de basismethode Exercise 4: Codeer een eenvoudige regressie met één variabele

Huidige oefening

Exercise 5: Een model onderzoeken Exercise 6: Voorspellen nadat je een model hebt gefit Exercise 7: Voorspellen met het werkloosheidsmodel Exercise 8: Multivariate lineaire regressie (deel 1)Exercise 9: Multivariate lineaire regressie (deel 2)Exercise 10: Afronding van lineaire regressie

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.

Exercise 1: Evaluating a model graphically Exercise 2: Graphically evaluate the unemployment model Exercise 3: The gain curve to evaluate the unemployment model Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Calculate RMSE Exercise 6: R-squared Exercise 7: Calculate R-squared Exercise 8: Correlation and R-squared Exercise 9: Properly Training a Model Exercise 10: Generating a random test/train split Exercise 11: Train a model using test/train split Exercise 12: Evaluate a model using test/train split Exercise 13: Create a cross validation plan Exercise 14: Evaluate a modeling procedure using n-fold cross-validation

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.

Exercise 1: Categorical inputs Exercise 2: Examining the structure of categorical inputs Exercise 3: Modeling with categorical inputs Exercise 4: Interactions Exercise 5: Modeling an interaction Exercise 6: Modeling an interaction (2)Exercise 7: Transforming the response before modeling Exercise 8: Relative error Exercise 9: Modeling log-transformed monetary output Exercise 10: Comparing RMSE and root-mean-squared Relative Error Exercise 11: Transforming inputs before modeling Exercise 12: Input transforms: the "hockey stick"Exercise 13: Input transforms: the "hockey stick" (2)

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.

Exercise 1: Logistic regression to predict probabilities Exercise 2: Fit a model of sparrow survival probability Exercise 3: Predict sparrow survival Exercise 4: Poisson and quasipoisson regression to predict counts Exercise 5: Poisson or quasipoisson Exercise 6: Fit a model to predict bike rental counts Exercise 7: Predict bike rentals on new data Exercise 8: Visualize the bike rental predictions Exercise 9: GAM to learn non-linear transforms Exercise 10: Writing formulas for GAM models Exercise 11: Writing formulas for GAM models (2)Exercise 12: Model soybean growth with GAM Exercise 13: Predict with the soybean model on test data

In this chapter we will look at modeling algorithms that do not assume linearity or additivity, and that can learn limited types of interactions among input variables. These algorithms are *tree-based* methods that work by combining ensembles of *decision trees* that are learned from the training data.

Exercise 1: The intuition behind tree-based methods Exercise 2: Predicting with a decision tree Exercise 3: Random forests Exercise 4: Build a random forest model for bike rentals Exercise 5: Predict bike rentals with the random forest model Exercise 6: Visualize random forest bike model predictions Exercise 7: One-Hot-Encoding Categorical Variables Exercise 8: vtreat on a small example Exercise 9: Novel levels Exercise 10: vtreat the bike rental data Exercise 11: Gradient boosting machines Exercise 12: Find the right number of trees for a gradient boosting machine Exercise 13: Fit an xgboost bike rental model and predict Exercise 14: Evaluate the xgboost bike rental model Exercise 15: Visualize the xgboost bike rental model