sampling

• Data sampling is a technique used in machine learning to create a subset of the original data for training, validation, or testing a machine learning model. The goal of data sampling is to reduce the computational complexity and memory requirements of the machine learning algorithm, while still preserving the important characteristics of the data and avoiding overfitting.
• There are several types of data sampling methods used in machine learning, including:
  • 1. Random sampling: Random sampling is a simple and widely used method for creating a subset of the original data. In random sampling, a fixed number or percentage of data points are randomly selected from the original data, without any consideration of the underlying distribution or characteristics of the data.
  • 2. Stratified sampling: Stratified sampling is a method used to ensure that the subset of data is representative of the original data with respect to a specific attribute or class label. In stratified sampling, the data is divided into strata based on the values of the attribute or class label, and a fixed number or percentage of data points are randomly selected from each stratum.
  • 3. Oversampling: Oversampling is a method used to increase the number of data points in a specific class or group that is underrepresented in the original data. In oversampling, new data points are generated by resampling the existing data points from the underrepresented class, or by synthesizing new data points using techniques such as data augmentation or generative models.
  • 4. Undersampling: Undersampling is a method used to reduce the number of data points in a specific class or group that is overrepresented in the original data. In undersampling, a subset of the existing data points from the overrepresented class is randomly selected or chosen based on some criteria.
• Data sampling is an important technique in machine learning, as it can help to reduce the computational and memory requirements of the machine learning algorithm and improve the generalization performance of the model. However, the choice of data sampling method depends on the specific problem and the characteristics of the data, and may require experimentation and tuning to achieve the best results.