COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Anomaly detection
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Regression
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Classification
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Feature Learning
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Detailed explanation-1: -Classification Algorithms The output variable has to be a discrete value. The regression algorithm’s task is mapping input value (x) with continuous output variable (y). The classification algorithm’s task mapping the input value of x with the discrete output variable of y. They are used with continuous data.
Detailed explanation-2: -Unlike regression, the output variable of Classification is a category, not a value, such as “Green or Blue", “fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.
Detailed explanation-3: -Classification of DL approaches. DL techniques are classified into three major categories: unsupervised, partially supervised (semi-supervised) and supervised.
Detailed explanation-4: -Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters. Given recent user behavior, classify as churn or not.