Pemodelan keluaran moneter yang ditransformasi log

Dalam latihan ini, Anda akan berlatih membuat model pada keluaran moneter yang ditransformasi log, lalu mengonversi kembali prediksi "log-uang" ke satuan moneter. Data yang dimuat merekam pendapatan subjek pada tahun 2005 (Income2005), serta hasil beberapa tes kemampuan yang diikuti subjek pada tahun 1981:

Arith
Word
Parag
Math
AFQT (Persentil pada Armed Forces Qualifying Test)

Data sudah dipisah menjadi himpunan latih dan uji (income_train dan income_test) dan telah dimuat sebelumnya. Anda akan membangun model log(pendapatan) dari masukan tersebut, lalu mengonversi log(pendapatan) kembali menjadi pendapatan.

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

Petunjuk latihan

Panggil summary() pada income_train$Income2005 untuk melihat statistik ringkas pendapatan di himpunan latih.
Tulis sebuah formula untuk mengekspresikan log(Income2005) sebagai fungsi dari lima tes sebagai variabel fmla.log. Cetak variabel tersebut.
Sesuaikan model linear log(Income2005) pada data income_train: model.log.
Gunakan model.log untuk memprediksi pendapatan pada himpunan data income_test. Simpan di kolom logpred.
- Periksa summary() dari logpred untuk melihat bahwa skala besarnya sangat berbeda dari Income2005.
Balikkan transformasi log agar prediksi menjadi dalam "satuan moneter": exp(income_test$logpred).
- Periksa summary() dari pred.income dan lihat bahwa skalanya kini mirip dengan skala Income2005.
Isi bagian yang kosong untuk membuat scatter plot pendapatan terprediksi vs. pendapatan pada himpunan uji.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Examine Income2005 in the training set
___

# Write the formula for log income as a function of the tests and print it
(fmla.log <- ___)

# Fit the linear model
model.log <-  ___

# Make predictions on income_test
income_test$logpred <- ___
summary(income_test$logpred)

# Convert the predictions to monetary units
income_test$pred.income <- ___
summary(income_test$pred.income)

#  Plot predicted income (x axis) vs income
ggplot(___, aes(x = ___, y = ___)) + 
  geom_point() + 
  geom_abline(color = "blue")

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: 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: Masukan kategorikal Exercise 2: Menelaah struktur masukan kategorikal Exercise 3: Pemodelan dengan masukan kategorikal Exercise 4: Interaksi Exercise 5: Memodelkan sebuah interaksi Exercise 6: Memodelkan interaksi (2)Exercise 7: Mentransformasikan respons sebelum pemodelan Exercise 8: Galat relatif Exercise 9: Pemodelan keluaran moneter yang ditransformasi log

Latihan Saat Ini

Exercise 10: Membandingkan RMSE dan Root-Mean-Squared Relative Error Exercise 11: Mentransformasikan input sebelum pemodelan Exercise 12: Transformasi input: "hockey stick"Exercise 13: Transformasi input: "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