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.

Welcome to the course!

Why tree-based methods?

Specify that tree

Train that model

How to grow your tree

Train/test split

Avoiding class imbalances

From zero to hero

Predict and evaluate

Make predictions

Crack the matrix

Are you predicting correctly?

Classification Trees

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.

Continuous outcomes

Train a regression tree

Predict new values

Inspect model output

Performance metrics for regression trees

In-sample performance

Out-of-sample performance

Bigger mistakes, bigger penalty

Cross-validation

Create the folds

Fit the folds

Evaluate the folds

Bias-variance tradeoff

Call things by their names

Adjust model complexity

In-sample and out-of-sample performance

Regression Trees and Cross-Validation

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.

Tuning hyperparameters

Generate a tuning grid

Tune along the grid

Pick the winner

More model measures

Calculate specificity

Draw the ROC curve

Area under the ROC curve

Bagged trees

Create bagged trees

In-sample ROC and AUC

Check for overfitting

Random forest

Bagged trees vs. random forest

Variable importance

Hyperparameters and Ensemble Models

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.

Pengantar boosting

Bagging vs. boosting

Menentukan ensemble boosted

Gradient boosting

Latih sebuah boosted ensemble

Evaluasi ansambel

Bandingkan dengan satu pengklasifikasi

Optimalkan ansambel boosting

Persiapan penyetelan

Penyetelan yang sebenarnya

Finalisasi model

Perbandingan model

Bandingkan AUC

Plot kurva ROC

Penutup

Boosted Trees

Chocolate ratings

Diabetes risk

Bank customer churn

Model Machine Learning berbasis pohon dapat mengungkap hubungan nonlinier yang kompleks dalam data dan sering mendominasi kompetisi Machine Learning. Dalam kursus ini, Anda akan menggunakan paket tidymodels untuk mengeksplorasi dan membangun berbagai model berbasis pohon—dari pohon keputusan sederhana hingga random forest yang kompleks. Anda juga akan mempelajari boosted trees, teknik Machine Learning yang kuat yang menggunakan ensemble learning untuk membangun model prediktif berkinerja tinggi. Sepanjang perjalanan, Anda akan bekerja dengan data kesehatan dan risiko kredit untuk memprediksi insiden diabetes dan churn pelanggan.

Modeling with tidymodels in R

Pelajari cara menggunakan model berbasis pohon dan ensemble untuk prediksi dengan tidymodels.

Machine Learning dengan Model Berbasis Pohon di R

Pelajari cara menggunakan model berbasis pohon dan ensambel untuk melakukan prediksi klasifikasi dan regresi dengan tidymodels.

Dasar-Dasar Pembelajaran Mesin di R

Ilmuwan Pembelajaran Mesin di R

Pembelajaran Mesin Terawasi di R

Bagging vs. boosting

Machine Learning dengan Model Berbasis Pohon di R

Latihan interaktif langsung