Ground Truth

Ground truth

In the field of machine learning (ML), ground truth refers to the correct or true labels or annotations for a given dataset. Ground truth is used to evaluate the performance of an ML model, as well as to train and validate the model.

For example, if an ML model is being developed to classify images of animals, the ground truth would be the correct labels for each image, such as "cat" "dog" or "bird" The model would be trained on a dataset that includes both the images and their corresponding ground truth labels, and its performance would be evaluated based on how accurately it is able to predict the correct labels for new, unseen images.

For large datasets in particular, obtaining ground truth labels can be a time-consuming and labor-intensive procedure. It frequently entails manually going through and annotating each case in the dataset, which can take a lot of time. It may be possible in some circumstances to establish ground truth labels using automated approaches, but these procedures may be less dependable and necessitate more manual evaluation and correction.

Ground truth is a critical element of machine learning since it offers a standard by which to compare an ML model's performance. It is also a key element of many ML tasks, such as supervised learning, where the model is trained and validated using the ground truth labels. Contrarily, in unsupervised learning, the model must learn to recognise patterns and correlations in the data without any explicit direction because ground truth labels may not be accessible.

One platform for creating better training data and debugging models.

Check it out on Github

What is ground truth for machine learning?

Ground truth is a critical element of machine learning since it offers a standard by which to compare an ML model's performance. It is also a key element of many ML tasks, such as supervised learning, where the model is trained and validated using the ground truth labels. Contrarily, in unsupervised learning, the model must learn to recognise patterns and correlations in the data without any explicit direction because ground truth labels may not be accessible.