Prediksi kelangsungan hidup burung pipit

Pada latihan ini, Anda akan memprediksi probabilitas kelangsungan hidup menggunakan model kelangsungan hidup burung pipit dari latihan sebelumnya.

Ingat bahwa saat memanggil predict() (docs) untuk mendapatkan probabilitas prediksi dari model glm(), Anda harus menyatakan bahwa Anda menginginkan respons:

predict(model, type = "response")

Jika tidak, predict() pada model regresi logistik akan mengembalikan log-odds kejadian yang diprediksi, bukan probabilitasnya.

Anda juga akan menggunakan fungsi GainCurvePlot() (docs) untuk memplot kurva gain dari prediksi model. Jika kurva gain model mendekati kurva gain ideal ("wizard"), maka model mengurutkan burung pipit dengan baik: yaitu, model memprediksi bahwa burung pipit yang benar-benar bertahan hidup akan memiliki probabilitas kelangsungan hidup yang lebih tinggi. Masukan untuk fungsi GainCurvePlot() adalah:

frame: data frame dengan kolom prediksi dan kolom kebenaran dasar
xvar: nama kolom prediksi (sebagai string)
truthVar: nama kolom dengan keluaran aktual (sebagai string)
title: judul untuk plot (sebagai string)

GainCurvePlot(frame, xvar, truthVar, title)

Data frame sparrow dan model sparrow_model telah dimuat sebelumnya.

Latihan ini adalah bagian dari kursus

Supervised Learning di R: Regresi

Petunjuk latihan

Buat kolom baru di sparrow bernama pred yang berisi prediksi pada data pelatihan.
Panggil GainCurvePlot() untuk membuat kurva gain dari prediksi. Apakah model tersebut mampu mengurutkan burung pipit dengan baik berdasarkan apakah mereka benar-benar bertahan hidup atau tidak?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# sparrow is available
summary(sparrow)

# sparrow_model is available
summary(sparrow_model)

# Make predictions
sparrow$pred <- ___

# Look at gain curve
___(___, ___, ___, "sparrow survival 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: 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: Regresi logistik untuk memprediksi probabilitas Exercise 2: Sesuaikan model probabilitas kelangsungan hidup burung gereja Exercise 3: Prediksi kelangsungan hidup burung pipit

Latihan Saat Ini

Exercise 4: Regresi Poisson dan quasipoisson untuk memprediksi cacah Exercise 5: Poisson atau quasipoisson Exercise 6: Melatih model untuk memprediksi jumlah sewa sepeda Exercise 7: Prediksi penyewaan sepeda pada data baru Exercise 8: Visualisasikan prediksi penyewaan sepeda Exercise 9: GAM untuk mempelajari transformasi nonlinier Exercise 10: Menulis rumus untuk model GAM Exercise 11: Menulis rumus untuk model GAM (2)Exercise 12: Memodelkan pertumbuhan kedelai dengan GAM Exercise 13: Memprediksi dengan model soybean pada data uji

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