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(Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning is a rapidly developing field that seeks to create algorithms and models that can learn from data to make predictions or decisions. For these models to be accurate, they need to be trained on high-quality data - including "ground truth." Ground truth is a key concept in machine learning, defined as accurate and reliable information about the target variable or phenomenon being learned by the model. T...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
Machine learning is a rapidly developing field that seeks to create | [[Machine learning]] is a rapidly developing field that seeks to create [[algorithm]]s and [[model]]s that can learn from [[data]] to make [[prediction]]s or decisions. For these models to be accurate, they need to be trained on high-quality data - including [[ground truth]]. | ||
Ground truth is a key concept in machine learning, defined as accurate and reliable information about the target variable or phenomenon being learned by the model. The quality of ground truth data significantly affects the precision and dependability of its predictions | [[Ground truth]] is a key concept in machine learning, defined as accurate and reliable information about the target variable or phenomenon being learned by the model. The quality of ground truth data significantly affects the [[precision]] and [[dependability]] of its predictions. | ||
==Importance of Ground Truth== | ==Importance of Ground Truth== | ||
It is critical that the data used to train a machine learning model be of high quality. | It is critical that the data used to train a [[machine learning model]] be of high quality. If the [[training data]] is [[noisy]], incomplete, [[biased]], or mis[[label]]ed, the model won't perform well in real life. Thus, it must be ensured that this training data accurately represents the target variable. | ||
Ground truth data is an indispensable source of reliable information for training machine learning models. It serves as the "gold standard" against which predictions are measured and evaluated. Without accurate ground truth data, it would be impossible to assess the accuracy and efficiency of a model's predictions. | Ground truth data is an indispensable source of reliable information for training machine learning models. It serves as the "gold standard" against which predictions are measured and evaluated. Without accurate ground truth data, it would be impossible to assess the accuracy and efficiency of a model's predictions. | ||