APPLICATION OF SUPERVISED LEARNING
SUPPORT VECTOR MACHINE SVM
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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All data is unlabelled and the algorithms learn to inherent structure from the input data
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All data is labeled and the algorithms learn to predict the output from the input data
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It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction
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Some data is labeled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used.
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Detailed explanation-1: -Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.
Detailed explanation-2: -A supervised learning algorithm takes a known set of input data (the learning set) and known responses to the data (the output), and forms a model to generate reasonable predictions for the response to the new input data. Use supervised learning if you have existing data for the output you are trying to predict.
Detailed explanation-3: -Supervised learning: predicting an output variable from high-dimensional observations. Supervised learning consists in learning the link between two datasets: the observed data X and an external variable y that we are trying to predict, usually called “target” or “labels”.