APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Dataset
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Classifiers
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supervised learning
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unsupervised learning
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Detailed explanation-1: -Supervised: All data is labeled and the algorithms learn to predict the output from the input data. Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data.
Detailed explanation-2: -Common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest, which are described in more detail below.
Detailed explanation-3: -A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.
Detailed explanation-4: -Unsupervised learning refers to algorithms that are provided with labeled data.