Supervised Learning Algorithms: 3 Main Types❑ Use labeled training data to learn mapping function 𝑭 that turns inputvariables (𝑋) into the output variable 𝑌 :𝑌 = 𝐹(𝑋)1. Classificationo Using inputs to predict the input outcome in form of categories.o Response output variable y is qualitative or categoricalo Eg: use Apple data to classify its sector as Technology, Consumer,...o Eg, Clustering, PCA, Decision trees2. Regressiono Using inputs to predict the outcome in real values.o Response output variable y is quantitativeo Eg, using Apple data to predict its future stock price.o Eg. Linear Reg, Logistic Reg, CART, NaÔve-Bayes, K-Nearest Neighbor
Question
Supervised Learning Algorithms: 3 Main Types❑ Use labeled training data to learn mapping function 𝑭 that turns inputvariables (𝑋) into the output variable 𝑌 :𝑌 = 𝐹(𝑋)1. Classificationo Using inputs to predict the input outcome in form of categories.o Response output variable y is qualitative or categoricalo Eg: use Apple data to classify its sector as Technology, Consumer,...o Eg, Clustering, PCA, Decision trees2. Regressiono Using inputs to predict the outcome in real values.o Response output variable y is quantitativeo Eg, using Apple data to predict its future stock price.o Eg. Linear Reg, Logistic Reg, CART, NaÔve-Bayes, K-Nearest Neighbor
Solution
The text you provided describes three main types of supervised learning algorithms. These are:
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Classification: This type of algorithm uses input data to predict the outcome in the form of categories. The response output variable (y) is qualitative or categorical. For example, using Apple's data to classify its sector as Technology, Consumer, etc. Some examples of classification algorithms include Clustering, PCA, and Decision Trees.
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Regression: This type of algorithm uses input data to predict the outcome in real values. The response output variable (y) is quantitative. For example, using Apple's data to predict its future stock price. Some examples of regression algorithms include Linear Regression, Logistic Regression, CART, Naive-Bayes, and K-Nearest Neighbor.
The text does not mention a third type of supervised learning algorithm. However, it's worth noting that there are other types of supervised learning algorithms such as Support Vector Machines (SVM), Random Forest, and Neural Networks.
Similar Questions
Explain the mechanism of supervised learning as a fundamental approach in machine learning. Emphasizing how algorithms map inputs to outputs. Provide an example of a supervised learning algorithm, such as logistic regression, and describe its application in a specific domain
What is a supervised learning algorithm?Select one:a.An algorithm that can only perform classification tasksb.An algorithm that can only perform regression tasksc.An algorithm that can learn from unlabeled datad.An algorithm that can learn from labeled data
Which of the following best describes supervised learning?Answer areaThe model learns from data without any labeled responsesThe model is trained on labeled dataThe model learns by optimizing a fitness functionThe model operates on symbolic data rather than numerical data
What is the primary goal of training a supervised learning model?<br /> A. 1. Minimize error between predicted and actual outputs <br />B. 2. Classify data into categories <br />C. 3. Optimize a function without labeled data <br />D. 4. Discover hidden patterns in data
What is an example of a classification task in supervised learning?Select one:a.Predicting the price of a house based on its characteristicsb.Determining whether an email is spam or notc.Grouping customers into different segments based on their spending habitsd.Predicting the stock price for the next day based on historical data
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