Memodelkan sebuah interaksi

Dalam latihan ini, Anda akan menggunakan interaksi untuk memodelkan pengaruh jenis kelamin dan aktivitas lambung terhadap metabolisme alkohol.

Data frame alcohol telah dimuat sebelumnya, dan memiliki kolom:

Metabol: laju metabolisme alkohol
Gastric: laju aktivitas enzim alkohol dehidrogenase di lambung
Sex: jenis kelamin peminum (Male atau Female)

Dalam video, kita memasangkan tiga model pada data alcohol:

satu dengan hanya term aditif (efek utama): Metabol ~ Gastric + Sex
dua model, masing-masing dengan interaksi antara aktivitas lambung dan jenis kelamin

Anda melihat bahwa salah satu model dengan term interaksi memiliki R-squared yang lebih baik daripada model aditif, yang menunjukkan bahwa penggunaan term interaksi memberikan kecocokan yang lebih baik. Dalam latihan ini, Anda akan membandingkan R-squared dari salah satu model interaksi dengan model yang hanya berisi efek utama.

Ingat bahwa operator : menyatakan interaksi antara dua variabel. Operator * menyatakan interaksi antara dua variabel tersebut, ditambah efek utamanya.

x*y = x + y + x:y

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

Petunjuk latihan

Tulis sebuah formula yang menyatakan Metabol sebagai fungsi dari Gastric dan Sex tanpa interaksi.
- Tetapkan formula tersebut ke variabel fmla_add dan cetak.
Tulis sebuah formula yang menyatakan Metabol sebagai fungsi dari interaksi antara Gastric dan Sex.
- Tambahkan Gastric sebagai efek utama, tetapi bukan Sex.
- Tetapkan formula tersebut ke variabel fmla_interaction dan cetak.
Pasangkan model linear hanya dengan efek utama: model_add pada data.
Pasangkan model linear dengan interaksi: model_interaction pada data.
Panggil summary() pada kedua model. Mana yang memiliki R-squared lebih baik?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# alcohol is available
summary(alcohol)

# Create the formula with main effects only
(fmla_add <- ___ )

# Create the formula with interactions
(fmla_interaction <- ___ )

# Fit the main effects only model
model_add <- ___

# Fit the interaction model
model_interaction <- ___

# Call summary on both models and compare
___
___

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

Latihan Saat Ini

Exercise 6: Memodelkan interaksi (2)Exercise 7: Mentransformasikan respons sebelum pemodelan Exercise 8: Galat relatif Exercise 9: Pemodelan keluaran moneter yang ditransformasi log 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