La curva di guadagno per valutare il modello di disoccupazione

Nell'esercizio precedente hai fatto previsioni su female_unemployment e hai visualizzato le previsioni e i residui. Ora tracerai anche la curva di guadagno delle previsioni di unemployment_model rispetto ai valori reali di female_unemployment usando la funzione WVPlots::GainCurvePlot() (docs).

Quando l'ordine conta più dei valori esatti, la curva di guadagno ti aiuta a verificare se le previsioni del modello ordinano nello stesso modo dell'esito reale.

Le chiamate alla funzione GainCurvePlot() sono del tipo:

GainCurvePlot(frame, xvar, truthvar, title)

dove

frame è un data frame
xvar e truthvar sono stringhe che indicano i nomi delle colonne del data frame frame con le previsioni e con l'esito reale
title è il titolo del grafico

Quando le previsioni ordinano esattamente nello stesso modo, il coefficiente di Gini relativo è 1. Quando il modello ordina male, il coefficiente di Gini relativo è vicino a zero, o persino negativo.

Il data frame unemployment, che contiene anche le previsioni, e il modello unemployment_model sono a tua disposizione.

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

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Istruzioni dell'esercizio

Carica il pacchetto WVPlots usando library().
Traccia la curva di guadagno. Dai al grafico il titolo "Unemployment model". Le previsioni del modello ordinano correttamente?

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# unemployment (with predictions) is available
summary(unemployment)

# unemployment_model is available
summary(unemployment_model)

# Load the package WVPlots
___

# Plot the Gain Curve
___(___, ___, ___, "Unemployment model")

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Supervised Learning in R: Regression

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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: Welcome and Introduction Exercise 2: Identify the regression tasks Exercise 3: Linear regression - the fundamental method Exercise 4: Code a simple one-variable regression Exercise 5: Examining a model Exercise 6: Predicting once you fit a model Exercise 7: Predicting from the unemployment model Exercise 8: Multivariate linear regression (Part 1)Exercise 9: Multivariate linear regression (Part 2)Exercise 10: Wrapping up linear regression

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: Valutare un modello graficamente Exercise 2: Valuta graficamente il modello sulla disoccupazione Exercise 3: La curva di guadagno per valutare il modello di disoccupazione

Esercizio in corso

Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Calcolare l'RMSE Exercise 6: R-quadro Exercise 7: Calcolare R-squared Exercise 8: Correlazione e R-quadro Exercise 9: Addestrare correttamente un modello Exercise 10: Generare una suddivisione casuale train/test Exercise 11: Addestra un modello usando il train/test split Exercise 12: Valutare un modello con train/test split Exercise 13: Crea un piano di cross validation Exercise 14: Valutare una procedura di modellazione con la cross-validation a n fold

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