How does the size of the training data affect the accuracy?

Kayley Marshall
Kayley MarshallAnswered

The precision of a machine learning model is very sensitive to the quantity of the training data. In most cases, the model’s accuracy improves as the size of the training dataset grows.

Advantages of bigger training datasets

More information being provided to the model is a key reason why bigger training datasets might lead to improved accuracy. Machine learning models are built to gain knowledge from the information in the data they are fed during training. If the dataset is large enough, the model can spot trends and correlations it would have missed with a smaller sample. Furthermore, the training model accuracy improves as the amount of available data increases.

Larger training datasets may improve accuracy in machine learning for another reason: they lessen the likelihood of overfitting the data. When a model is excessively intricate, it may provide a perfect match to the training data, but it will not generalize well to new data. Having additional data to train the model on may limit the model’s complexity, lessening the risk of overfitting.

Disadvantages of bigger training datasets

A bigger dataset does not always result in better accuracy! Because of the potential for noise, outliers, and irrelevant information that a bigger dataset with poor data quality might contribute, the model becomes less accurate. Guarantee that the data is useful, complete, and representative.

  • Gathering and keeping massive volumes of data may be costly.

The computing resources needed to train the model may increase if the dataset size is huge. That’s why you should consider the pros and cons of amassing a more comprehensive data collection before diving in.


A machine learning model’s accuracy may be greatly affected by the quantity of its training data. Only if the data is of high quality can a bigger dataset improve the model’s generalization capabilities. Large data sets, however, may drain your resources and are expensive to store. To assure the quality of the data, consider the costs and advantages of collecting and utilizing a bigger dataset.

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