1. Resampling for limited data
Welcome back! In this video, we'll explore resampling, a useful technique when data is limited.
2. What is resampling?
Imagine you have a jar full of marbles in different colors, and you want to understand the mix of colors without counting every marble. Instead of checking all of them, you take multiple samples by picking marbles, recording their colors, and repeating the process.
Resampling is like this, it involves drawing multiple samples from a dataset to gain insights, test patterns, or estimate uncertainty without analyzing the entire population at once.
3. Resampling techniques
Common resampling techniques we'll discuss in this lesson are bootstrapping, cross-validation, and synthetic sampling.
4. Bootstrapping
Bootstrapping works by repeatedly sampling from the dataset with replacement, meaning that some values may appear multiple times while others may not appear in a given sample. Each resampled dataset can then be used to for example calculate a specific statistic like the mean or median. By running this process thousands of times, we obtain a distribution of the statistics, which can then be analyzed to estimate variability and confidence intervals.
5. Cross-validation
Cross-validation is a resampling technique used in machine learning, to create training sets to make the model and validation sets to test the model. It involves resampling the data multiple times without replacement, ensuring that each value or group of values appears exactly once in one of the validation sets. This ensures the model can handle variability in the data.
6. Synthetic resampling
Synthetic sampling is another form of resampling, but instead of drawing subsets from existing data, it creates new, synthetic data points to improve dataset balance. By generating artificial examples, often by interpolating between real data points, synthetic sampling expands the dataset, helping models generalize better, especially when dealing with class imbalance in classification problems.
7. Example: fraud detection
I'll give you an example. A major challenge in fraud detection is the imbalance of datasets, where fraudulent transactions make up only a small fraction of all transactions. This imbalance can lead to machine learning models being biased toward the majority class, making them ineffective at detecting fraud.
To address this, a bank can apply synthetic sampling to artificially generate additional fraudulent transactions based on existing ones.
8. Example: fraud detection
Suppose a bank processes 1,000,000 transactions per month, with only 1,000 (0.1%) classified as fraudulent.
By applying synthetic sampling, the number of fraudulent transaction samples is increased synthetically to 10,000, making up 1% of the dataset.
This allows the model to learn patterns associated with fraudulent activity more effectively. The bank can then use this model to flag suspicious transactions more accurately, allowing for quicker intervention and fraud prevention.
9. Let's practice!
OK, time to practice!