What is the main difference between regression and classification tasks in supervised learning?Regression deals with discrete data, while classification deals with continuous dataRegression predicts a continuous output, while classification predicts discrete labelsThere is no difference; they are the sameRegression is used for unsupervised learning, while classification is used for supervised learning
Question
What is the main difference between regression and classification tasks in supervised learning?Regression deals with discrete data, while classification deals with continuous dataRegression predicts a continuous output, while classification predicts discrete labelsThere is no difference; they are the sameRegression is used for unsupervised learning, while classification is used for supervised learning
Solution
The main difference between regression and classification tasks in supervised learning is that regression predicts a continuous output, while classification predicts discrete labels.
Similar Questions
What is the difference between regression and classification?1 pointMachine learning models can be trained to do regression, but not classification.In regression, the input data is structured, but in classification the input data is unstructured.In regression, the correct answers have numerical value, but in classification the correct answers are categories.Machine learning models can be trained to do classification, but not regression.6.Question 6
Regression vs. Clustering vs. Classification*
Define classification and prediction. How are they different?
What is an example of a regression 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
12. Which of the following statement correctly indicates the difference between ‘supervised learning’ and ‘unsupervised learning’?以下哪一個説法正確地指出「監督式學習」和「非監督式學習」的區別?Supervised learning cannot be applied to regression analysis, while unsupervised learning can. 監督式學習不可用於迴歸分析;非監督式學習則可以。Unsupervised learning requires a labeled dataset for training, while supervised learning can extract features and patterns from unlabeled dataset for learning. 非監督式學習需要利用已標注的數據集來進行學習;監督式學習則可以從未標注的數據集上歸納出特徵和模式。Supervised learning requires a labeled dataset for learning, while unsupervised can extract features and patterns from unlabeled dataset for learning. 監督式學習需要利用已標注的數據集來進行學習;非監督式學習則可以從未標注的數據集上歸納出特徵和模式。Supervised learning can be applied to clustering analysis, while unsupervised learning cannot. 監督式學習可用於分群分析;非監督式學習則不可以。
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