Posted On: Apr 01, 2020
The training data is used by the algorithm to create a model. It used this data to learn and fit the model. In a dataset, about 60 to 80 percent of data is allocated as training data. The testing data is used to test the accuracy of the model trained with the training data. The model from the training data predicts the testing data to see how well it works. Separating the dataset into training and testing data is important as you can minimize the effect of data discrepancies and better understand the characteristics of the model.
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Supervised and unsupervised are the two types of Machine learning algorithms available. In the supervised type, the algorithms are applied to the known labeled data to formulate a model. Labeled data...