Supervised learning requires labeled data as training cases.
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Supervised learning methods need annotated data in order to generate efficient models.
Supervised learning is used when training data is labeled and the CR has prior information about the environment.
Supervised learning is highly susceptible to "data overfitting," whereby analytical models that are built to explain how a system works are overly fine-tuned to include and account for every variable and historical data point.
Supervised learning using external sources of data for estimating model parameters is robust to biased sampling in the study sample but is sensitive to the reference panels used for training (Zadrozny et al. 2003).
Supervised learning requires a large amount of labeled data, but the data labeling process can be expensive and time consuming, as it requires the efforts of human experts.
Supervised learning requires the use of labelled data on which an algorithm is trained, and training sets are called labeled data because the value for the class label is known.
Supervised learning works very well when labeled data is available.
Supervised learning methods are widely used to associate two independent data sets; the model of the subtype is defined in a "training" data set, and assessed in a "test" data set.
Supervised learning refers to machine learning that uses past data to make predictions, often times with equations so long and complex that they're completely opaque to human interpretability and understanding.
