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Which of the following learning approaches is supervised learning?
Linear regression is a supervised learning technique typically used in predicting, forecasting, and finding relationships between quantitative data. It is one of the earliest learning techniques, which is still widely used.
Is a simple approach to supervised learning it assumes?
Explanation: Linear regression is a simple approach to supervised learning. It assumes that the dependence of Y on X1, X2, . . . Explanation: When there are more than one independent variables in the model, then the linear model is termed as the multiple linear regression model.
What’s the difference between supervised and unsupervised learning?
To put it simply, supervised learning uses labeled input and output data, while an unsupervised learning algorithm does not. In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer.
Which is an example of a supervised learning task?
For example, current hot image classification is a classification task, and prediction of stock price is a regression task. The procedure of Supervised Learning can be described as the follows: we use x(i) to denote the input variables, and y(i) to denote the output variable.
How does supervised learning learn from labeled data?
In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Classification predicts the category the data belongs to.
Which is performance measure used in supervised learning?
This particular performance measure is called accuracy and it is often used in classification tasks as it is a supervised learning approach. In supervised learning, algorithms learn from labeled data.
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