What Is a Dataset?

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When a collection of data is organized in a specific manner, such as a table or other schema, a dataset is created. Organizing the data helps you interpret its most critical elements and gain new insight, such as patterns and trends.

Data is essential to artificial intelligence projects because it trains the model’s algorithms to produce outcomes. However, raw data is often unstructured. If data is introduced into the model without first being structured, it will produce inaccurate outcomes.

 

What Makes a Good Dataset?

If you want a “good dataset,” you’ll need to know what factors matter most to the model. Ideally, your dataset should be:

• Relevant: The accuracy of your model’s predictions will primarily depend on the data used to train it. Suppose you are developing a local app that responds to voice commands. In that case, you need voice data that includes accents, colloquial terms, and slang for the application to understand and deliver results.

• Extensive in Coverage: Datasets are subject to biases and blind spots, making them imbalanced. When used to train a model, their scope may be more limited than expected, producing inaccurate predictions. Determine what your model will cover and collect different but relevant data; the more varied a dataset is, the wider the range of results.
• Sufficient in Volume: Quality is not the only characteristic of a good dataset; quantity also matters. Having enough data to train the algorithm allows it to yield more accurate predictions over time. Even though it’s rare, using too much data is not ideal, possibly leading to problematic results. 

Also, keep in mind that it’s best to use an actual dataset instead of a fake one to see how precise the model’s predictions are in real-life applications. Although fabricated datasets are readily accessible and available in large volumes, the results can be too predictable or unpredictable when fed into the model. Adjusting the model based on such data to get the intended results may translate to inaccurate outcomes once you use real data.

 

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