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Introducción
Este módulo comienza con una pregunta guía.
Elige una de las siguientes respuestas:
Si tuvieras que priorizar la mejora de una de las siguientes áreas en tu proyecto de aprendizaje automático, ¿cuál tendría el mayor impacto?
Mejora la calidad de tu conjunto de datos
Los datos tienen prioridad sobre todo.
La calidad y el tamaño del conjunto de datos son mucho más importantes de lo que
el algoritmo más brillante que usas para crear tu modelo.
Aplicar una función de pérdida más inteligente para entrenar tu modelo
Una mejor función de pérdida puede ayudar
a que un modelo se entrene más rápido,
sigue siendo un segundo elemento de esta lista.
Esta es una pregunta aún más sugestiva:
Adivina: En tu proyecto de aprendizaje automático, ¿cuánto tiempo
sueles invertir en la preparación
y transformación de los datos?
Más de la mitad del tiempo del proyecto
Sí, quienes practican el AA dedican la mayor parte de su tiempo
a la construcción de conjuntos de datos y a la ingeniería de atributos.
Menos de la mitad del tiempo del proyecto
Planifica más. Por lo general, el 80% del tiempo de un proyecto de aprendizaje automático se dedica a la construcción de conjuntos de datos y a la transformación de datos.
En este módulo, aprenderás más sobre las características de los conjuntos de datos del aprendizaje automático y cómo preparar tus datos para garantizar resultados de alta calidad cuando entrenes y evalúes tu modelo.
[null,null,["Última actualización: 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)"]]
