Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Hutan keputusan memberikan manfaat berikut:
Model ini lebih mudah dikonfigurasi daripada jaringan saraf. Hutan keputusan
memiliki lebih sedikit hyperparameter; selain itu, hyperparameter dalam hutan
keputusan memberikan default yang baik.
Model ini menangani secara native fitur numerik, kategoris, dan yang tidak ada. Artinya,
Anda dapat menulis kode prapemrosesan yang jauh lebih sedikit daripada saat menggunakan jaringan neural, sehingga menghemat waktu dan mengurangi sumber error.
Model ini sering kali memberikan hasil yang baik secara langsung, tahan terhadap data yang berisi derau,
dan memiliki properti yang dapat ditafsirkan.
Model ini menyimpulkan dan melatih set data kecil (< 1 juta contoh) jauh lebih cepat daripada neural network.
Hutan keputusan menghasilkan hasil yang luar biasa dalam kompetisi machine learning, dan
sangat banyak digunakan dalam banyak tugas industri.
Kursus ini memperkenalkan pohon keputusan dan hutan keputusan.
Hutan keputusan adalah kumpulan algoritma machine learning yang dapat ditafsirkan yang unggul dalam menangani data tabular.
Hutan keputusan dapat melakukan:
Kursus ini menjelaskan cara kerja hutan keputusan tanpa berfokus pada library
tertentu.
Namun, di sepanjang kursus, kotak teks menampilkan contoh kode yang mengandalkan
library hutan keputusan YDF, tetapi dapat dikonversi ke library hutan
keputusan lainnya.
Prasyarat
Kursus ini mengasumsikan bahwa Anda telah menyelesaikan kursus berikut atau memiliki pengetahuan
yang setara:
[null,null,["Terakhir diperbarui pada 2025-02-25 UTC."],[[["\u003cp\u003eDecision forests are interpretable machine learning algorithms that work well with tabular data for tasks like classification, regression, and ranking.\u003c/p\u003e\n"],["\u003cp\u003eDecision forests offer advantages such as easy configuration, native handling of various data types, robustness to noise, and fast inference/training on smaller datasets.\u003c/p\u003e\n"],["\u003cp\u003eThis course provides a comprehensive understanding of decision trees and forests, including how they make predictions, different types, performance considerations, and effective usage strategies.\u003c/p\u003e\n"],["\u003cp\u003eThe course uses YDF library code examples to demonstrate concepts, but the knowledge is transferable to other decision forest libraries.\u003c/p\u003e\n"],["\u003cp\u003eBasic machine learning knowledge and familiarity with data preprocessing are prerequisites for this course.\u003c/p\u003e\n"]]],[],null,["# Introduction\n\n\u003cbr /\u003e\n\n| **Estimated Course Time:** 2.5 hours\n\nDecision forests provide the following benefits:\n\n- They are **easier to configure** than neural networks. Decision forests have **fewer hyperparameters** ; furthermore, the hyperparameters in decision forests provide **good defaults**.\n- They **natively handle** numeric, categorical, and missing features. This means you can write far less preprocessing code than when using a neural network, saving you time and reducing sources for error.\n- They often give **good results out of the box**, are robust to noisy data, and have interpretable properties.\n- They infer and train on small datasets (\\\u003c 1M examples) **much faster** than neural networks.\n\nDecision forests produce great results in machine learning competitions, and\nare heavily used in many industrial tasks.\n\nThis course introduces decision trees and decision forests.\nDecision forests are a family of\n[interpretable](/machine-learning/glossary#interpretability) machine learning\nalgorithms that excel with tabular data.\nDecision forests can perform:\n\n- [Classification](https://developers.google.com/machine-learning/glossary/#classification-model)\n- [Regression](https://developers.google.com/machine-learning/glossary/#regression-model)\n- [Ranking](https://developers.google.com/machine-learning/glossary/#ranking)\n- [Anomaly detection](https://developers.google.com/machine-learning/glossary#anomaly-detection)\n- [Uplift modeling](https://developers.google.com/machine-learning/glossary/#uplift-modeling).\n\n| **Learning Objectives:**\n|\n| - Explain decision trees and decision forests.\n| - Determine how decision trees and decision forests make predictions.\n| - Understand how different types of decision forests, such as random forests and gradient boosted trees.\n| - Explain when decision forests perform well, and what their limitations are.\n- Develop a sense of how to use decision forests effectively. \nYDF Code\nThis course explains how decision forests work without focusing on any specific libraries. However, throughout the course, text boxes showcase code examples that rely on the [YDF](https://ydf.readthedocs.io) decision forest library, but can be be converted to other decision forest libraries.\n\nPrerequisites\n-------------\n\nThis course assumes you have completed the following courses or have equivalent\nknowledge:\n\n- [Machine Learning Crash Course](/machine-learning/crash-course)\n- [Machine Learning Problem Framing](/machine-learning/problem-framing)\n- [Data Prep and Feature Engineering](/machine-learning/data-prep)\n\n*Happy Learning!*"]]
