Question

A training set based on feeding 60% of data, validating on 20% of data, and then designing multiple tests for the remaining 20% of data is referred to as an

A: False positive set
B: Optimized training set
C: Predictive training set
D: Classic training set

Answer 1

The described methodology of splitting data into training, validation, and testing sets is known as a Classic training set, commonly used in machine learning processes.

Step-by-step explanation:

A training set involving feeding 60% of data, validating on 20% of data, and using the remaining 20% of data for multiple tests is referred to as a Classic training set. This method is a traditional approach in machine learning where the dataset is split into three parts. The largest portion is used for training the model, a smaller portion is used for validating and fine-tuning the model, and the last part is used to test the model's predictive power. This approach ensures that the model is not only tailored to the training data but also generalizes well to unseen data.