How much data do you need for machine learning, or how little? Graphite Note outlines how much data you need for machine learning models, predictive analytics, and machine learning algorithms.
Let’s set the scene for you with a machine learning problem. You’ve spent weeks building a machine learning model. You finally feed it data, and eagerly await the results. And then, the machine stumbles. The culprit? Not enough data. Data is the lifeblood of your machine learning project. Unlike a recipe, however, there’s no one-size-fits-all answer to the question: How much data do you need for machine learning?
Let’s move beyond the generic responses, and look ahead to equipping you with the right insights. As we move ahead, you’ll learn more about the factors that influence machine learning data needs. We’ll also outline practical mitigation strategies, and outline iterative approaches.
The importance of machine learning in various industries
Machine learning is a subset of Artificial Intelligence (AI). Machine learning focuses on building models that can learn from data. Data science uses scientific methods, machine learning algorithms, and systems, to extract knowledge and insights.
The role of data in machine learning
Data is the lifeblood of machine learning. Without data, there would be no way to train and test machine learning models. In the world of big data, how much data is enough? One of the most common questions asked is: how much data do you need for machine learning projects?
Factors that influence how much data you need for machine learning
When you develop a machine learning model, you need the right amount and quality of data. Datasets differ in many ways, and some machine learning models may need more data than others. Too little data, and you may not get good results. Insufficient data is a problem data scientists need to solve. These are the factors that define how much data you need for machine learning projects:
- The type of machine learning problem: Supervised learning models need labeled training data. Supervised learning models need more data than unsupervised models. Unsupervised models do not use labels. Image recognition or natural language processing (NLP) projects will need larger AI training data sets.
- The model complexity: The more complex a model is, the more data it will need. Models with many layers or nodes will need more training data than those with fewer layers or nodes. Models that use many algorithms will need more data than those that use only a single learning algorithm.
- The data quality and accuracy: Assess your raw data. If there is a lot of noise or incorrect information in your input data or test data, you will need to increase the dataset size. This will ensure you get accurate results. If there are missing values or outliers in the dataset, you must remove or assign them. That’s why it will be necessary to increase the dataset size.
Techniques to mitigate your data quantity limitations
You can estimate how much data you need for machine learning projects by using these data augmentation techniques:
- The rule-of-thumb approach: The rule-of-thumb approach is most often used with smaller datasets. This approach involves making an estimation, based on past experiences and current knowledge. The rule-of-thumb rule is that you need at least ten times as many data points as there are features in your dataset. For example, if your dataset has 10 columns or features, you should have at least 100 rows. The rule-of-thumb approach ensures that enough high-quality input exists. The rule-of-thumb approach also helps you to avoid common pitfalls. These include data sample bias and underfitting during post-deployment phases. The rule-of-thumb approach also helps to achieve predictive capabilities faster.
Statistical methods to estimate sample size: You need to use statistical methods to estimate sample size with larger datasets. These methods enable you to calculate the number of data samples required to ensure accuracy and reliability. Several strategies can reduce the amount of data needed for an ML model. You can use feature selection techniques, like principal component analysis (PCA) and recursive feature elimination (RFE), to identify and remove redundant features from a dataset. Dimensionality reduction techniques, like singular value decomposition (SVD) and t-distributed stochastic neighbor embedding (t-SNE), can lower the number of dimensions in a dataset while preserving important information. You can use synthetic data generation techniques, like generative adversarial networks (GANs) can to generate more training examples from existing datasets.
Tips to reduce how much data you need for machine learning
While bigger data often means better results, it’s not always a must. Here are some practical tips to reduce how much data you need for machine learning with:
- Pre-trained models: Power up your machine learning model with pre-trained models. Pre-trained models provide pre-existing knowledge. For example, ResNet for image recognition or BERT for natural language processing. Fine-tune pre-trained models on your specific task with a smaller dataset, and voila! You have a powerful model without needing vast initial training data.
- Transfer learning: You’ve trained a machine learning model (ML model) to identify cats in images. Now, you want to detect dogs. Instead of starting from scratch, use transfer learning. Leverage the cat-recognizing features and adapt them to identify dogs, using a smaller dataset of dog images. This “knowledge transfer” saves time, resources, and data.
- Feature engineering: No two data points are equal. Identify the features that matter for your prediction task. Then, cut out irrelevant data. This reduces complexity and allows your machine learning model (ML models) to learn from a concise dataset with good data points.
- Dimensionality reduction: Sometimes, your data has high dimensionality, with many features. While useful, it can burden your model and need more data. Techniques like Principal Component Analysis (PCA) compress data. PCA identifies key patterns and reduces dimensions while preserving essential information. Think of it as summarizing a book’s main points instead of reading every word.
- Active learning: Let your machine learning model guide its data journey. Instead of passively consuming everything, active learning algorithms query for the most informative data points. This targeted approach ensures the model learns from the most effective data. Your machine learning model is better equipped to achieve good results with fewer samples.
- Data augmentation: Don’t limit your model to the data you have. Techniques, including image flipping, text synonym replacement, or synthetic data points, expand your dataset. This diversity helps your machine learning model generalize better and perform well.
- Try different combinations: Experimentation can help to further enhance your results. Try different combinations of these techniques. See what works best for your specific task and dataset. More complex models may require you to try more complex combinations too.
Examples of machine learning with small datasets
- According to a survey conducted by Kaggle in 2020, 70% of respondents said they had completed a ML project with fewer than 10,000 samples. More than half of the respondents said they had completed a project with fewer than 5,000 samples.
- A team at Stanford University used a dataset of only 1,000 images to create an AI system that could diagnose skin cancer.
- A team at MIT used a dataset of only 500 images to create an AI system that could detect diabetic retinopathy in eye scans.
Machine learning data and ethics
While how much data you need for machine learning drives your approach, there are ethical considerations. Selecting your AI training set for your AI project must be considered carefully. Collecting personal information raises questions around consent, transparency, and regulatory compliance. You must consider the ethical ramifications and use case of your machine learning project. Machine learning models that are trained on limited datasets can have inherent biases. Inherent biases can perpetuate or amplify inequalities. Diverse, representative datasets should always be your goal.
