What is Model Selection in Machine Learning?

Solution(By Examveda Team)

Model selection in machine learning refers to the process of choosing the most appropriate model or algorithm from a set of candidate models to make predictions or capture relationships within a given dataset.

Option A: The process of selecting models among different mathematical models, which are used to describe the same data set.
This option correctly defines model selection in machine learning. It involves comparing and choosing from different mathematical models to find the one that best fits and describes the data.

Option B: when a statistical model describes random error or noise instead of the underlying relationship.
This statement appears to describe a situation where a model fails to capture the true underlying relationship in the data and instead models random error or noise. However, it is not the primary definition of model selection.

Option C: Find interesting directions in data and find novel observations/database cleaning.
This option seems to describe the process of exploratory data analysis and data preprocessing rather than model selection itself.

Option D: All above.
This option suggests that all of the statements (A, B, and C) are correct definitions of model selection. While option A is indeed a correct definition, options B and C are not. Therefore, Option D is not the correct choice.

In conclusion, the correct definition of model selection in machine learning is Option A: The process of selecting models among different mathematical models, which are used to describe the same data set.