Kurva gain untuk mengevaluasi model pengangguran

Pada latihan sebelumnya Anda membuat prediksi tentang female_unemployment dan memvisualisasikan prediksi serta residualnya. Sekarang, Anda juga akan membuat plot kurva gain dari prediksi unemployment_model dibandingkan female_unemployment aktual menggunakan fungsi WVPlots::GainCurvePlot() (docs).

Untuk situasi ketika urutan lebih penting daripada nilai tepatnya, kurva gain membantu Anda memeriksa apakah prediksi model mengurutkan dengan urutan yang sama seperti keluaran sebenarnya.

Pemanggilan fungsi GainCurvePlot() berbentuk:

GainCurvePlot(frame, xvar, truthvar, title)

di mana

frame adalah sebuah data frame
xvar dan truthvar adalah string yang menamai kolom prediksi dan kolom keluaran aktual dari frame
title adalah judul plot

Ketika prediksi diurutkan persis dalam urutan yang sama, koefisien Gini relatif bernilai 1. Ketika model mengurutkan dengan buruk, koefisien Gini relatif mendekati nol, atau bahkan negatif.

Data frame unemployment, yang juga memuat prediksi, dan model unemployment_model tersedia untuk Anda gunakan.

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

Petunjuk latihan

Muat paket WVPlots menggunakan library().
Plot kurva gain. Beri judul plot "Unemployment model". Apakah prediksi model terurut dengan benar?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

# unemployment_model is available
summary(unemployment_model)

# Load the package WVPlots
___

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

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

SkillTag.level.intermediateSkillTag.label

4.6+

Mulai Kursus 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: 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: Mengevaluasi model secara grafis Exercise 2: Evaluasi model pengangguran secara grafis Exercise 3: Kurva gain untuk mengevaluasi model pengangguran

Latihan Saat Ini

Exercise 4: Root Mean Squared Error (RMSE)Exercise 5: Hitung RMSE Exercise 6: R-squared Exercise 7: Hitung R-squared Exercise 8: Korelasi dan R-squared Exercise 9: Melatih Model dengan Tepat Exercise 10: Membuat pembagian acak data train/test Exercise 11: Latih model menggunakan pemisahan test/train Exercise 12: Evaluasi model menggunakan pembagian train/test Exercise 13: Buat rencana cross validation Exercise 14: Evaluasi prosedur pemodelan menggunakan 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