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==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. 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.
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 [[mislabel]]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.