What is cross-validation?
A)It is the assessment of a model's level of errors across different samples, which is useful as it allows us to assess how many errors there might be in a model using different samples.
B)It is the comparison of your findings with the work of others, using the same data.
C)It is the use of theoretical literature to build your valid data model.
D)It is the assessment of a model's accuracy across different samples, which is useful as it allows us to assess how well our model might predict the outcome in different samples.

Question

Quizplus · Accepted Answer

Cross-validation is the process of evaluating the performance of a machine learning model on an independent dataset. It involves dividing the dataset into training and testing sets, fitting the model on the training set, and then evaluating its performance on the testing set. This process is repeated multiple times with different partitions of the dataset to assess the model's accuracy and ability to generalize to new data. Option D correctly describes cross-validation as the assessment of a model's accuracy across different samples, which allows us to assess how well our model might predict the outcome in different samples.

What Is Cross-Validation