Anatomi Model Machine Learning

Sekarang, Anda akan memperkuat pemahaman tentang bagaimana data memengaruhi kinerja model. Anda akan bekerja dengan himpunan data pemesanan Airbnb (dalam berkas booking.csv). Himpunan data ini cocok untuk tugas klasifikasi guna memprediksi apakah seseorang akan membatalkan pemesanan. Data berisi beberapa kolom numerik dan kategorikal. Anda akan membagi himpunan data yang disediakan menjadi tiga sampel yang saling eksklusif — train_A.csv, train_B.csv, dan test.csv — menggunakan skrip split_dataset.py. Selanjutnya, untuk masing-masing himpunan data pelatihan, Anda akan menjalankan pipeline pemrosesan data dan pelatihan model untuk melatih model Random Forest Classifier dan menguji kinerjanya pada himpunan uji menggunakan model_training.py. Hiperparameter yang didefinisikan di params.json konsisten pada kedua kali pelatihan.

Skrip Python dirancang untuk menerima argumen baris perintah dan dijalankan melalui shell. Silakan jelajahi skrip-skrip ini untuk memperkaya pemahaman Anda.

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Pengantar Versioning Data dengan DVC

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Pengantar Versioning Data dengan DVC

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This chapter provides a comprehensive introduction to Data Version Control (DVC), a tool essential for data versioning in machine learning. Learners will explore the motivation behind data versioning, understand its differences from code versioning, and experiment with a simple classification problem. They will review basic Git commands, learn about DVC, and practice setting up a repository. The chapter concludes with an overview of DVC’s features and use cases, including versioning data and models, CI/CD for machine learning, experiment tracking, pipelines, and more.

Exercise 1: Motivasi Versioning Data Exercise 2: Anatomi Model Machine Learning

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Exercise 3: Perbedaan antara Versioning Data dan Kode Exercise 4: Memahami Hiperparameter Exercise 5: Pengenalan DVC Exercise 6: Bekerja dengan Git CLI Exercise 7: Tinjau DVC CLI Exercise 8: Fitur DVC dan kasus penggunaan Exercise 9: Pipeline DVC Exercise 10: CI/CD untuk machine learning

This chapter delves into the setup of DVC, encompassing aspects such as installation, initialization of the repository, and the utilization of the .dvcignore file. It further navigates through the exploration of DVC cache and staging files, imparting knowledge on how to add and remove files, manage caches, and comprehend the underlying mechanisms using the MD5 hash. The chapter also elucidates on DVC remotes, distinguishing them from Git remotes, and guides you on how to add, list, and modify them. Lastly, it teaches you how to interact with these remotes by pushing and pulling data, checking out specific versions, and fetching data to the cache.

Exercise 1: DVC Setup and Initialization Exercise 2: Setting up DVC Exercise 3: .dvcignore Patterns Exercise 4: DVC Cache and Staging Files Exercise 5: Working with DVC Cache Exercise 6: Understanding .dvc Files Exercise 7: Configuring DVC Remotes Exercise 8: Purpose of DVC Remotes Exercise 9: Setup a DVC Remote Exercise 10: Interacting with DVC Remotes Exercise 11: Versioning Data using DVC Remote Exercise 12: Checking out Versioned Data