Boosted bir topluluk belirt

Boosting, bagging'den daha gelişmiştir çünkü tüm modellerin paralel öğrenmesi yerine, modeller art arda eğitilir ve her model bir öncekini iyileştirir. Bu güçlü algoritmayı uygulayan birkaç R paketi vardır ve tidymodels işini kolaylaştırır. Boosted ağaçlar oluşturmak için basit bir arayüz sunar.

Aşağıdaki üç adımda görevin, sonraki egzersizlerde kullanacağın boosted ağaçlardan oluşan bir topluluğun başlangıç belirtimini oluşturmaktır.

Bu egzersiz

R ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Specify the model class
boost_spec <-___()

boost_spec

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Ağaç Tabanlı Modellerle Machine Learning

kursunun bir parçasıdır

InicianteNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions Exercise 11: Crack the matrix Exercise 12: Are you predicting correctly?

Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.

Exercise 1: Continuous outcomes Exercise 2: Train a regression tree Exercise 3: Predict new values Exercise 4: Inspect model output Exercise 5: Performance metrics for regression trees Exercise 6: In-sample performance Exercise 7: Out-of-sample performance Exercise 8: Bigger mistakes, bigger penalty Exercise 9: Cross-validation Exercise 10: Create the folds Exercise 11: Fit the folds Exercise 12: Evaluate the folds Exercise 13: Bias-variance tradeoff Exercise 14: Call things by their names Exercise 15: Adjust model complexity Exercise 16: In-sample and out-of-sample performance

Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.

Exercise 1: Tuning hyperparameters Exercise 2: Generate a tuning grid Exercise 3: Tune along the grid Exercise 4: Pick the winner Exercise 5: More model measures Exercise 6: Calculate specificity Exercise 7: Draw the ROC curve Exercise 8: Area under the ROC curve Exercise 9: Bagged trees Exercise 10: Create bagged trees Exercise 11: In-sample ROC and AUC Exercise 12: Check for overfitting Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Variable importance

Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.

Exercise 1: Boosting’e giriş Exercise 2: Bagging vs. boosting Exercise 3: Boosted bir topluluk belirt

Geçerli Egzersiz

Exercise 4: Gradient boosting Exercise 5: Güçlendirilmiş bir ensemble eğit Exercise 6: Ansambılı değerlendir Exercise 7: Tek bir sınıflandırıcıyla karşılaştır Exercise 8: Boosted topluluğu optimize et Exercise 9: Ayara hazırlık Exercise 10: Gerçek ayarlama Exercise 11: Modeli sonlandır Exercise 12: Model karşılaştırması Exercise 13: AUC'yi karşılaştır Exercise 14: ROC eğrilerini çiz Exercise 15: Kapanış