Data downsampling

It entails selecting a subset of data points from the original dataset in a way that represents the overall distribution and trends of the data. 

Downsampling is achieved through various methods such as random sampling, systematic sampling, or stratified sampling. It is often used to improve computational efficiency, reduce storage requirements, and make data analysis more manageable.

Why is data downsampling important? 

By selecting a subset of data points that represent the overall distribution and trends of the data, downsampling can improve computational efficiency, reduce storage requirements, and make data analysis more manageable.

Here’s how downsampling techniques help machine learning and data science teams improve the efficiency of their models. 

• Reduces the computational time and storage requirements associated with large datasets, making data analysis more efficient and scalable. 
• Preserves the essential characteristics of the data while reducing its size, allowing for more manageable and interpretable results.
• Mitigates the effects of data imbalance, where certain classes or categories are overrepresented or underrepresented in the dataset. By downsampling the majority class or upsampling the minority class, data scientists can create a more balanced dataset for machine learning models.

Types of data downsampling

There’s a variety of data sampling techniques for data preprocessing that focus on different goals and are not equally accurate. 

Below, we explore the most ubiquitous downsampling methods, keeping in mind that the selection of downsampling techniques should follow a case-by-case approach. 

• Random sampling: selecting data points randomly from the original dataset.
• Systematic sampling: selecting data points at regular intervals from the original dataset.
• Stratified sampling: dividing the dataset into subgroups based on specific criteria and then sampling from each subgroup.
• Cluster sampling: grouping data points into clusters and then randomly selecting clusters to sample from.
• Aggregated sampling: combining multiple data points into a single aggregated value.
• Decimation: reducing the sampling rate of a time series dataset.
• Quantization: reducing the precision of numerical data.
• Dimensional reduction: reducing the number of features in a dataset.

Which downsampling method is a better fit for your project? 

• For datasets with a uniform distribution, random or systematic sampling may be sufficient. 
• For datasets with non-uniform distributions or specific subgroups, stratified or cluster sampling can be more effective. 
• Aggregated sampling is suitable for reducing the granularity of time series data or numerical data. 
• Decimation and quantization are commonly used for reducing the sampling rate or precision of time series data. 
• Dimensionality reduction techniques are useful for reducing the number of features in high-dimensional datasets.