COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Supervised
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Unsupervised
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Reinforcement Learning
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deep-learning
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Detailed explanation-1: -Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.
Detailed explanation-2: -Types of supervised Machine learning Algorithms: Linear Regression. Regression Trees. Non-Linear Regression. Bayesian Linear Regression.
Detailed explanation-3: -Labeled data is used in supervised learning, whereas unlabeled data is used in unsupervised learning .
Detailed explanation-4: -For supervised learning to work, you need a labeled set of data that the model can learn from to make correct decisions. Data labeling typically starts by asking humans to make judgments about a given piece of unlabeled data.
Detailed explanation-5: -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.