APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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You’re running a company, and you want to develop learning algorithms to address each of two problems. Problem 1:You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.Problem 2:You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. Should you treat these as classification or as regression problems?
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Treat both as classification problems.
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Treat problem 1 as a classification problem, problem 2 as a regression problem.
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Treat problem 1 as a regression problem, problem 2 as a classification problem.
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Treat both as regression problems.
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Explanation:
Detailed explanation-1: -Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables. We can derive this structure by clustering the data based on relationships among the variables in the data.
Detailed explanation-2: -Logistic Regression It can be used for the classification problems in machine learning, and the output of the logistic regression algorithm can be either Yes or NO, 0 or 1, Red or Blue, etc.
Detailed explanation-3: -The most commonly used unsupervised learning algorithms are: K-means clustering. Hierarchical clustering. Apriori algorithm.
There is 1 question to complete.