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Introdução
Este módulo começa com uma pergunta introdutória.
Escolha uma das seguintes respostas:
Se você teve que priorizar a melhoria de uma das áreas a seguir
no projeto de machine learning, que teria a maior
impacto?
Como melhorar a qualidade do seu conjunto de dados
Os dados são mais importantes.
A qualidade e o tamanho do conjunto de dados são muito mais importantes do que o algoritmo que você usa para criar o modelo.
Como aplicar uma função de perda mais inteligente ao treinamento do modelo
Verdadeiro. Uma função de perda melhor pode ajudar um modelo a treinar mais rapidamente, mas
ainda está a um segundo item da lista.
E aqui vai uma pergunta ainda mais sugestiva:
Tente adivinhar: em seu projeto de machine learning, quanto tempo
você normalmente gasta com preparação e transformação de dados?
Mais da metade do tempo do projeto
Sim, os profissionais de ML passam a maior parte do tempo construindo conjuntos de dados e fazendo engenharia de atributos.
Menos da metade do tempo do projeto
Planeje mais! Normalmente, 80% do tempo em uma equipe
projeto é gasto construindo conjuntos de dados e transformando dados.
Neste módulo, você vai saber mais sobre as características do machine learning
conjuntos de dados e como prepará-los para garantir resultados de alta qualidade ao
para treinar e avaliar seu modelo.
[null,null,["Última atualização 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)"]]
