Learn how to improve model accuracy with diverse, balanced, and large dataset with this guide. Find out what these terms mean, why they are important, and how they affect the AI model performance.
Table of Contents
Question
What is a benefit of a diverse, balanced, and large dataset?
A. Training time
B. Data privacy
C. Model accuracy
Answer
C. Model accuracy
Explanation
The correct answer is C. Model accuracy. A diverse, balanced, and large dataset is a benefit for model accuracy, as it can help to train an AI model that can generalize well to new and unseen data. A diverse dataset contains data from various sources, domains, and perspectives, which can help to capture the complexity and variability of the real-world data. A balanced dataset contains data that is evenly distributed across different categories, classes, or groups, which can help to avoid overfitting or underfitting the data. A large dataset contains data that is sufficient and representative of the data population, which can help to reduce the noise and uncertainty in the data. By using a diverse, balanced, and large dataset, an AI model can learn from high-quality data and produce accurate and reliable predictions or outcomes.
Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data.
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