Certain AI tasks demand numeric predictions rather than classifications. Learn when and how to apply regression algorithms to forecast continuous values like future sea levels.
You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use?
The correct answer is B. regression.
Regression is a type of supervised machine learning that is used to predict numeric values, such as sea level, temperature, sales, etc. Regression models learn the relationship between input features and a continuous target variable, and then use this relationship to make predictions on new data.
Classification is another type of supervised machine learning that is used to predict categorical values, such as labels, classes, categories, etc. Classification models learn the relationship between input features and a discrete target variable, and then use this relationship to assign a class to new data.
Clustering is a type of unsupervised machine learning that is used to discover groups of similar data points, without using any labels or target variables. Clustering models learn the structure and patterns in the data, and then use this structure to assign a cluster to new data.
In this scenario, the task is to predict the sea level in meters for the next 10 years, which is a numeric value. Therefore, regression is the most suitable type of machine learning to use. You can use various regression models in Azure Machine Learning designer, such as Linear Regression, Neural Network Regression, or Boosted Decision Tree Regression.
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset.
The trained model can then be used to make predictions.
Microsoft Docs > Previous Versions > Module Categories and Descriptions > Machine Learning Modules > Initialize Model > Regression > Linear Regression
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