Benchmarking is a process of comparing different models or algorithms to determine which is best for a given task or data set. Benchmarking is a critical step in the development of machine learning models, as it helps engineers and data scientists select the most accurate and efficient model for a specific task.
In benchmarking, the performance of different models is compared using a metric or set of metrics that reflect the prediction quality or accuracy of the model. Some common metrics include accuracy, average accuracy, sensitivity and specificity. More advanced performance measures, such as area under the curve (AUC) or log loss, may also be used.
Benchmarking may also involve the use of cross-validation techniques, where the dataset is divided into training and test sets, and each model is trained and tested on different subsets of the data to avoid overfitting.
The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
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