Highlights –

  • A statistical modeling error called overfitting happens when a function is too closely matched to a small number of data points.
  • Discover the essential tactics for avoiding overfitting and obtaining precise results from small data.

The terms big data and data science are frequently used in conjunction. With large amounts of data being produced every day, data scientists are expected to be able to gain essential insights from all of the data available. Of course, it is possible!

Practically speaking, you will frequently have limited information to solve an issue. Compiling an extensive dataset can be prohibitively expensive or even impossible (e.g., only having records from a specific time when doing time series analysis). As a result, working with a small dataset and making accurate predictions with it is the only option left.

This article will briefly discuss the issues that can arise when working with a small dataset. Furthermore, we’ll talk about the best solutions to these issues.

What is small data?

In contrast to big data, small data is information that comes in small quantities and is frequently understandable by humans. Sometimes, small data can be a subset of a larger dataset that characterizes a specific group.

Models trained on small datasets are more likely to identify patterns that don’t exist, which leads to high variance and very high error on a test set. These are some of the typical symptoms of overfitting.

A statistical modeling error called overfitting happens when a function is too closely matched to a small number of data points. Because of this, the model is only helpful concerning its original data set and not any other data sets. When working with small datasets, the aim should be yo avoid overfitting.

The seven best methods to avoid overfitting when using small datasets are as follows:

Pick basic models: Complex models that have specific parameters are more likely to result in overfitting:

  • Consider using logistic regression as your first step when training a classifier.
  • Consider a simple linear model with a constrained number of weights if you train a model to forecast a specific value.
  • Limit the maximum depth for tree-based models.
  • Regularization methods can help a model remain more conservative.

Your objective with limited data is to prevent the model from detecting relationships and patterns that don’t exist. This means you should eliminate any model that implies non-linearity or feature interactions and limits the number of weights and parameters. Also, according to research, some classifiers may perform better with small datasets.

Eliminate outliers from data: Outliers can significantly affect the model when working with a small dataset. As a result, when working with limited data, you must recognize and eliminate outliers. Yet another strategy is to use methods that are robust to outliers, such as quantile regression. Eliminating the impact of outliers is necessary to arrive at a helpful model with a small dataset.

Pick important features: Explicit feature selection is typically not the best method, but when data is scarce, it may be necessary. With few observations and many predictors, it is challenging to avoid overfitting. There are various methods to select features, such as importance analysis, analysis of correlation with a target variable, and recursive elimination. It is also important to note that choosing features will always benefit from domain knowledge. So, if you are unfamiliar with the topic, seek a domain expert to go over the feature selection process.

Integrate various models: When you combine the outcomes from multiple models, it is possible to obtain much more precise predictions. For instance, compared to the predictions from each model, the final projection calculated as the weighted average of forecasts from various unique models will have significantly lower variance and better generalizability. You can combine predictions from the same or different models using multiple hyperparameter values.

Instead of relying on point estimates, use confidence intervals:  In addition to the prediction, estimating confidence intervals for your hypothesis is frequently a good idea. When working with a small dataset, this becomes especially crucial. Therefore, when performing a regression analysis, remember to assess a 95% confidence interval. When resolving a classification problem, the probabilities of your class predictions should be calculated. You are less likely to draw the wrong conclusions from the model’s results if you better understand how “confident” your model is about its predictions.

Expand the dataset:  When data is highly scarce, or the dataset is severely unbalanced, look for ways to extend the dataset. You can, for instance:

  • Use synthetic samples: This is a typical strategy to address the underrepresentation of certain classes in a dataset. There are various methods to supplement datasets with synthetic samples. Pick the one that fits your specific task the best.
  • Combine data from other potential sources: For instance, if you’re modeling the temperature in a particular area, use weather data from other areas, but give more weight to the data points from your area of interest.

When appropriate, use transfer learning: This method falls under the category of data extension. Transfer learning entails developing a general model on large datasets that are readily available before optimizing it on your own small dataset. Suppose you’re working on an image classification problem, for instance. In that case, you can use a model already trained on ImageNet, a sizable image dataset, and then customize it for your issue. Compared to models created from scratch using scant data, pre-trained models are more likely to yield accurate predictions. Flexible deep-learning techniques are particularly effective for transfer learning.

More tips to manage small data challenges

In the opinion of many researchers and practitioners, small data is the future of data science. Large datasets cannot be used for every type of problem. To overcome the difficulties of a small dataset, here are a few recommendations:

  • Knowing the fundamentals of statistics will help you anticipate problems that may arise when working with few observations.
  • Discover the essential tactics to avoid overfitting and obtain precise results from small data.
  • Complete all data cleaning and analysis steps quickly (e.g., using Tidyverse in R language or Python tools for data science).
  • When concluding the model’s predictions, consider its limitations.

Small data can assist us in introducing more diversity into our product design and overcoming the issue of a “one size fits all” solution.

Working with small amounts of data is difficult. You must be inventive because the Machine Learning (ML) tools we use today are primarily made to work with Big Data. The problem with a small dataset is that in order to achieve accuracy, it’s very easy to overfit. However, various tools and techniques can be used to enhance the accuracy of your models. They can also be used to gain insights on smaller datasets.