Learn whether income is considered a label or feature when building a machine learning model to predict employee income ranges based on a dataset with income, postcode, and income range columns. Discover best practices for training accurate models.
Table of Contents
Question
You have been provided following dataset of employees:
Income | Postcode | Income Range |
---|---|---|
25000 | 2000 | Low |
32000 | 2002 | Medium |
95000 | 2000 | High |
You have to use the dataset to train a model that will Income range of employee.
Confirm that Income is a _____ .
Income Range is a _____ .
A.Label, Label
B. Feature, Feature
C. Label, Feature
D. Feature, Label
Answer
D. Feature, Label
Explanation
In the given dataset, Income is a Feature and Income Range is a Label when training a model to predict an employee’s income range category.
A Feature, also known as an input variable or predictor, is an individual measurable property or characteristic of the phenomenon being observed. Features are used as inputs to train a machine learning model. In this case, the Income column contains the annual income values for each employee, which can be used as a feature to help predict the corresponding Income Range.
A Label, also known as a target variable or output, is the final output or prediction that the model is trying to learn based on the input features. It’s the variable we want the model to predict. Here, the Income Range column categorizes incomes into “Low”, “Medium”, and “High” ranges, making it the label or target variable.
When training a supervised machine learning model, the goal is to learn the underlying patterns and relationships between the input features (e.g., Income) and the corresponding labels or targets (e.g., Income Range). The model learns from this labeled training data to make predictions on new, unseen data points.
Therefore, in this scenario, Income serves as a predictive Feature, while Income Range acts as the target Label that the model aims to predict accurately based on the given features.
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