Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Pengantar
Modul ini dimulai dengan pertanyaan utama.
Pilih salah satu jawaban berikut:
Jika Anda harus memprioritaskan peningkatan salah satu area berikut
dalam project machine learning, manakah yang akan memiliki dampak
terbesar?
Meningkatkan kualitas set data Anda
Data mengalahkan semuanya.
Kualitas dan ukuran set data jauh lebih penting daripada algoritma yang Anda gunakan untuk membuat model.
Menerapkan fungsi kerugian yang lebih cerdas untuk melatih model
Memang benar, fungsi loss yang lebih baik dapat membantu model dilatih lebih cepat, tetapi
masih jauh di bawah item lain dalam daftar ini.
Dan inilah pertanyaan yang jauh lebih penting:
Coba tebak: Dalam project machine learning Anda, berapa lama waktu
yang biasanya Anda habiskan untuk persiapan dan transformasi data?
Lebih dari setengah waktu proyek
Ya, praktisi ML menghabiskan sebagian besar waktu mereka
membangun set data dan
melakukan rekayasa fitur.
Kurang dari setengah waktu proyek
Rencanakan lebih banyak lagi. Biasanya, 80% waktu pada project
machine learning dihabiskan untuk membuat set data dan mengubah data.
Dalam modul ini, Anda akan mempelajari
lebih lanjut karakteristik machine learning
{i>dataset<i}, dan cara mempersiapkan data Anda untuk memastikan hasil yang berkualitas tinggi.
melatih dan mengevaluasi model Anda.
[null,null,["Terakhir diperbarui pada 2025-07-27 UTC."],[[["\u003cp\u003eThis module emphasizes the critical role of data quality in machine learning projects, highlighting that it significantly impacts model performance more than algorithm choice.\u003c/p\u003e\n"],["\u003cp\u003eMachine learning practitioners typically dedicate a substantial portion of their project time (around 80%) to data preparation and transformation, including tasks like dataset construction and feature engineering.\u003c/p\u003e\n"],["\u003cp\u003eThe module covers key concepts in data preparation, such as identifying data characteristics, handling unreliable data, understanding data labels, and splitting datasets for training and evaluation.\u003c/p\u003e\n"],["\u003cp\u003eLearners will gain insights into techniques for improving data quality, mitigating issues like overfitting, and interpreting loss curves to assess model performance.\u003c/p\u003e\n"],["\u003cp\u003eThis module builds upon foundational machine learning concepts, assuming familiarity with topics like linear regression, numerical and categorical data handling, and basic machine learning principles.\u003c/p\u003e\n"]]],[],null,["# Datasets, generalization, and overfitting\n\n| **Estimated module length:** 105 minutes\n| **Learning objectives**\n|\n| - Identify four different characteristics of data and datasets.\n| - Identify at least four different causes of data unreliability.\n| - Determine when to discard missing data and when to impute it.\n| - Differentiate between direct and derived labels.\n| - Identify two different ways to improve the quality of human-rated labels.\n| - Explain why to subdivide a dataset into a training set, validation set, and test set; identify a potential problem in data splits.\n| - Explain overfitting and identify three possible causes for it.\n| - Explain the concept of regularization. In particular, explain the following:\n| - Bias versus variance (adaptation to outliers...)\n| - L~2~ regularization, including Lambda (regularization rate)\n| - Early stopping\n| - Interpret different kinds of loss curves; detect convergence and overfitting in loss curves.\n| **Prerequisites:**\n|\n| This module assumes you are familiar with the concepts covered in the\n| following modules:\n|\n| - [Introduction to Machine Learning](/machine-learning/intro-to-ml)\n| - [Linear regression](/machine-learning/crash-course/linear-regression)\n| - [Working with numerical data](/machine-learning/crash-course/numerical-data)\n| - [Working with categorical data](/machine-learning/crash-course/categorical-data)\n\nIntroduction\n------------\n\nThis module begins with a leading question.\nChoose one of the following answers: \nIf you had to prioritize improving one of the following areas in your machine learning project, which would have the most impact? \nImproving the quality of your dataset \nData trumps all. The quality and size of the dataset matters much more than which shiny algorithm you use to build your model. \nApplying a more clever loss function to training your model \nTrue, a better loss function can help a model train faster, but it's still a distant second to another item in this list.\n\nAnd here's an even more leading question: \nTake a guess: In your machine learning project, how much time do you typically spend on data preparation and transformation? \nMore than half of the project time \nYes, ML practitioners spend the majority of their time constructing datasets and doing feature engineering. \nLess than half of the project time \nPlan for more! Typically, 80% of the time on a machine learning project is spent constructing datasets and transforming data.\n\nIn this module, you'll learn more about the characteristics of machine learning\ndatasets, and how to prepare your data to ensure high-quality results when\ntraining and evaluating your model. \n[Help Center](https://support.google.com/machinelearningeducation)"]]
