Derived vs. Obtained
What's the Difference?
Derived and obtained are both verbs that refer to acquiring or obtaining something. However, there is a subtle difference between the two. Derived typically implies that something is obtained from a source or origin, while obtained simply means to acquire or receive something. Derived suggests a more indirect or secondary way of obtaining something, while obtained is more straightforward.
Comparison
| Attribute | Derived | Obtained |
|---|---|---|
| Definition | Obtained from something else | Acquired or gained |
| Origin | Comes from a source or origin | Acquired through effort or action |
| Process | Result of a process or transformation | Result of an action or effort |
| Relationship | Connected to something else | Acquired through a means or method |
Further Detail
Definition
Derived attributes are those that are calculated or obtained from other attributes in a dataset. These attributes are not directly measured but are derived through a mathematical formula or algorithm. On the other hand, obtained attributes are those that are directly measured or collected from a source without any further calculation or manipulation.
Characteristics
Derived attributes are often used in data analysis to create new variables that provide additional insights into the dataset. These attributes can be created using various statistical methods such as regression analysis, clustering, or principal component analysis. Obtained attributes, on the other hand, are typically collected through surveys, experiments, or observations without any further manipulation.
Examples
An example of a derived attribute could be the average score of a group of students, which is calculated by summing up individual scores and dividing by the number of students. On the other hand, an obtained attribute could be the gender of the students, which is directly collected from a survey or registration form without any further calculation.
Usage
Derived attributes are often used in machine learning algorithms to improve the performance of models by providing additional information or features. These attributes can help in better predicting outcomes or patterns in the data. Obtained attributes, on the other hand, are used as input variables in the analysis without any further manipulation.
Challenges
One of the challenges of using derived attributes is the risk of introducing bias or errors in the analysis if the calculation is not done correctly. It is important to validate the derived attributes and ensure that they are accurately calculated. Obtained attributes, on the other hand, may face challenges related to data quality or missing values if the collection process is not well-designed.
Conclusion
In conclusion, derived and obtained attributes play different roles in data analysis and have their own set of characteristics and challenges. While derived attributes are created through calculations or algorithms to provide additional insights, obtained attributes are directly collected from a source without any further manipulation. Both types of attributes are important in data analysis and can be used in combination to gain a better understanding of the dataset.
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