It is the number of independent values or quantities which can be assigned to a statistical distribution. It is used in Hypothesis testing and chi-square test.

In the context of machine learning, the term “degrees of freedom” typically refers to the number of values in the final calculation of a statistic that are free to vary. It is crucial to understand degrees of freedom in various statistical tests and models. The concept is used in hypothesis testing, model evaluation, and understanding the variability in data.

For example, in linear regression, the degrees of freedom are associated with the number of data points minus the number of parameters estimated in the model. In simpler terms, it represents the number of values in the final calculation that are free to vary after certain constraints are imposed.

In a broader statistical context, degrees of freedom may also refer to the number of values or quantities in the final calculation of a statistic that are free to vary without violating any constraints or conditions.

When discussing degrees of freedom in an interview, it would be helpful to provide a specific example relevant to the context of the question asked, demonstrating your understanding of its application in machine learning models or statistical analyses.