What is the process of dividing the data into a training set and a test set called?Select one:a.Splittingb.Partitioningc.Samplingd.Segmenting

Step 1: Break Down the Problem

We need to determine the correct terminology that describes the process of dividing data into a training set and a test set.

Step 2: Relevant Concepts

In the context of data science and machine learning, the division of data into subsets is a common practice used to evaluate model performance. The terms involved generally refer to:

1. Splitting: Commonly refers to dividing data into different sets.
2. Partitioning: Typically relates to dividing data into distinct groups for analysis or operation.
3. Sampling: Involves selecting a subset from a larger dataset, but not necessarily for creating training and test sets.
4. Segmenting: Often pertains to dividing data based on specific characteristics or strategies.

Step 3: Analysis and Detail

Among the options:

• a. Splitting is widely recognized in machine learning for dividing datasets into training and testing subsets.
• b. Partitioning can also apply but is less commonly used in this specific context.
• c. Sampling does not accurately describe the division of the entire dataset for training and testing.
• d. Segmenting is more focused on demographic or characteristic-based divisions rather than the standard training/test split.

Step 4: Verify and Summarize

The term that precisely describes the division of data for model evaluation is "splitting." Therefore, while partitioning might also seem valid, "splitting" is overwhelmingly the term used in practice.

Final Answer

a. Splitting is the process of dividing the data into a training set and a test set.