The correct answer is C. Data Splitting.
Data splitting is the process of dividing a dataset into two or more subsets. The most common split is into a training set and a testing set. The training set is used to train the model, and the testing set is used to evaluate the model’s performance.
Data splitting is important because it allows us to assess the model’s performance on data that it has not seen before. This is important because it ensures that the model is not simply memorizing the training data, but is actually learning to generalize to new data.
There are a number of different ways to split a dataset. The most common method is to randomly split the data into two sets. However, other methods, such as stratified sampling, can be used to ensure that the training and testing sets are representative of the overall dataset.
Data splitting is an important part of machine learning. It allows us to assess the model’s performance and to ensure that the model is not simply memorizing the training data.
Here are brief explanations of the other options:
- Data Sampling: This is the process of selecting a subset of data from a larger dataset. Data sampling can be used to reduce the size of a dataset, to improve the performance of machine learning algorithms, or to make the data more representative of the population from which it was drawn.
- Data Cleaning: This is the process of identifying and correcting errors in data. Data cleaning can be a time-consuming and tedious process, but it is essential for ensuring the accuracy of machine learning models.
- Data Transformation: This is the process of converting data into a format that is more suitable for machine learning algorithms. Data transformation can include tasks such as normalizing data, scaling data, and feature extraction.