Discover how predictive analysis can estimate the number of overtime hours for delivery persons based on order volume. Explore the classification, clustering, and regression techniques used in this context. Gain insights into the application of predictive models in optimizing workforce management.
Predicting how many hours of overtime a delivery person will work based on the number of order received is an example of ________.
The correct answer is C. Regression.
Regression is a type of supervised learning that predicts a continuous numerical value based on input features. In this case, the input feature is the number of orders received, and the output value is the number of hours of overtime a delivery person will work. Regression can help estimate the relationship between the input and output variables and forecast future values based on new inputs. Regression can also measure the significance and strength of the predictors and the goodness of fit of the model.
Classification is a type of supervised learning that predicts a discrete categorical value based on input features. For example, predicting whether a delivery person will work overtime or not based on the number of orders received is a classification problem.
Clustering is a type of unsupervised learning that groups unlabeled data points into clusters based on their similarity. For example, grouping delivery persons based on their work patterns or preferences is a clustering problem.
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.
- Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
- Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
- Microsoft Learn > Azure > Machine Learning > Linear Regression component
- Microsoft Learn > Previous Versions > Module Categories and Descriptions > Machine Learning Modules > Initialize Model > Clustering modules
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