samplingpred

samplingpred is a Python library designed for simplifying the process of predictive sampling in machine learning workflows. It aims to provide a straightforward and efficient way to select subsets of data for various predictive tasks, such as model training, validation, and testing. The library focuses on offering a range of sampling strategies that can be applied to datasets represented as NumPy arrays or Pandas DataFrames.

The core functionality of samplingpred revolves around its `Sampler` class. Users can instantiate this class and then apply different sampling methods. These methods include random sampling, stratified sampling to maintain class proportions, and potentially more advanced techniques depending on the library's development. The library is built with ease of use in mind, allowing users to specify sampling parameters like the desired sample size or the number of samples to draw.

One of the key benefits of using samplingpred is its ability to abstract away the complexities of implementing various sampling algorithms from scratch. This can be particularly useful for data scientists who need to quickly experiment with different sampling approaches without getting bogged down in low-level implementation details. The library's integration with common data science libraries like NumPy and Pandas ensures smooth interoperability within existing workflows. Its objective is to streamline the data preparation phase for predictive modeling.