Determining how much data is required to train ml models (machine learning) is a critical yet complex task in developing accurate and reliable models. With more datasets available now than ever before, there is often a common misconception that more data equates to better results for machine learning models.
However, that may not always be the case. The volume of training data required depends on several key factors related to the problem complexity, model architecture, performance metrics, and more.
This article provides an analysis of these influencing elements along with rule-of-thumb guidelines and strategies data professionals employ to estimate appropriate data needs.
Multiple interdependent factors affect how much training data needs to be fed to the machine learning algorithms to enable robust and generalizable modeling. Understanding these factors is key to estimating appropriate machine learning model needs.
Firstly, you should consider what machine learning problem you're trying to solve with a machine learning algorithm. Here are a few examples:
The choice of ML model and machine learning techniques governs how much data it needs based on architectural complexity. For instance, nonparametric models like KNN that simply store train examples require large volumes of data to perform well.
Deep neural networks with increasing number of hidden layers and parameters also demand exponentially more data. That is because the intricate transformations learned by such models rely on seeing myriad examples for an accurate mapping between inputs and outputs.
On the other hand, linear/logistic regression models converge quickly with limited data owing to their basic functional form and lower parameterization. The move from simpler to more complex models must correspond with availability of larger training sets.
The complexity and type of input features are crucial in determining the needed data volume for machine learning:
The expected predictive performance and the tolerance for errors also plays into training data size estimation. For example, building ML models for sensitive use cases like fraud detection and medical diagnosis warrant higher precision and recall with lower failure rates.
To achieve such performance, significantly more relevant data is imperative during training and cross-validation. Likewise, exclusivity of misclassification costs and skew in class distributions necessitates gathering target class data through smart sampling.
The axiom of “garbage in, garbage out” holds strongly for machine learning. If the acquired training data carries too much noise or bias, then exponentially larger volumes would be required to extract meaningful signal. Presence of erroneous labels, missing values, outliers etc can be detrimental if not handled properly.
Hence, for deep learning algorithms it is critical to first understand data quality parameters like statistical distribution, redundancy, completeness and more through profiling. This knowledge helps estimate genuine training data needs once issues are mitigated via preprocessing and augmentation.
Although there are no one-size-fits-all rules for determining the exact amount of data needed, certain guidelines and statistical methods can offer a good starting point for estimating data requirements.
The 10 times rule is a useful starting heuristic for estimating dataset sizes in machine learning. It recommends having at least 10 examples for each feature or predictor variable in your model. For instance, if your model has 10 input features, then you would need at least 100 labeled training examples based on this guideline. While easy to apply, the 10x rule has some key limitations:
In practice, the central consideration is sufficiently covering the space of expected inputs and desired outputs. Both statistical and empirical analysis, starting from rules of thumb like 10x, help determine appropriate dataset sizes for reliable machine learning
Statistical power analysis provides a principled way for sample size estimation based on measurable factors like minimum required effect size, acceptable probability of errors, and population variance characteristics.
It offers a formalism to quantitatively translate such performance criteria into actual data volume requirements. This analysis can also help diagnose existing models to check if their post-hoc achieved performance justifies the sample size used during training.
Another heuristic is budgeting the dataset size as a function of the number of trainable model parameters. The rationale is that with higher parameterization, models should see more examples to reliably fit weights and biases.
A suggested formulation is having at least 10-20 samples per parameter in neural networks while some recent studies propose the inverse fraction of parameters as a measure instead. This methodology indirectly encodes model complexity into data needs.
In several real-world scenarios ranging from rare diseases to niche products, acquiring sizable training datasets is infeasible. In such cases, the following practical techniques can help reduce data needs without severely compromising model capability.
Data augmentation automatically expands existing datasets by applying transformations like cropping, rotation, translation etc. to generate modified instances. By creating such synthetic examples, models get exposed to new data with greater variability without manual data collection efforts. The key benefit is that perfect labeling is inherently preserved in the process.
Certain advanced methods like GANs can even generate highly realistic synthetic images and text. Such augmented data reduces the burden on original data volume needs significantly. However, safeguards must be deployed to prevent uncontrolled bias in the generated data.
Transfer learning allows leveraging knowledge gained in solving one problem for tackling a related new problem with limited data. It essentially transfers feature representations learned from large datasets to initialize models, fine-tuning only higher layers.
For computer vision, it is common to pretrain models on ImageNet, reuse learned visual features, and train task-specific classifiers with modest target data. This technique reduces the dependence on large application-specific training sets.
Feature selection eliminates input variables with low relevance, thereby reducing noise dimensions. It improves generalizability even with smaller sets through accentuation of core signals. Techniques like regularization impose constraints on model complexity by compressing feature space.
Feature engineering employs domain expertise to transform existing features into more informative representations for simpler problems. It also enables consolidating multiple correlated variables into fewer composite variables with retained variance. This shrinks the effective feature space to lower data needs.
Recent breakthroughs in computer vision, speech recognition and NLP have primarily been driven by deep neural networks. But their intimidatingly massive data appetite stems from specific structural and functional aspects:
The hierarchical layered architecture, dense interconnectivity and nonlinear activations equip DL models with exceptional fitting capacity for even arbitrarily complex functions. But opaqueness in learned transformations necessitates exhaustive examples.
Training procedures like backpropagation and gradient descent for thousands of parameters are inherently complicated by issues like vanishing gradients. Enormous data supplies continuous stimulation to emerge from suboptimal local minima.
High capacity models tend to easily overfit scarce data. Large and diverse datasets provide regularization effects during training to improve generalization. Additional strategies like dropout layers are also deployed.
Considering these facets, deep nets invariably call for enormous training sets to learn robust representations. For instance, popular ImageNet and BERT models were trained on millions of images and sentences respectively.
While abundant data powers most modern AI systems, some projects have managed to achieve decent success even with smaller datasets. Let us consider two such exemplary cases:
In a recent study published in the journal Sensors, researchers unveiled a groundbreaking development in the field of liver transplantation for patients with hepatitis C. The study introduced and successfully validated a machine learning (ML) model designed to predict short-term outcomes following liver transplantation in individuals with hepatitis C-related cirrhosis. Drawing data from a cohort of 90 liver transplant recipients, the ML model demonstrated remarkable accuracy, achieving performance levels between 99.76% and 100% in predicting postoperative complications within the first month after transplantation.
In the transportation sector, Tesla's Autopilot system relies heavily on object detection to perceive obstacles, vehicles, pedestrians etc. on the road and make driving decisions accordingly.
This allows advanced driver assistance features like automated emergency braking, lane centering, and self-parking. Object detection is key for autonomous vehicles to understand their surroundings and navigate safely. Another real world application is in security systems and surveillance. Object detection can automatically identify people, vehicles and objects in video feeds and images to monitor access control points.
For example, it can check if cars entering a parking garage have proper permits, or match license plates against databases. Retail stores also use object detection cameras to detect masks, and prevent thefts.
Determining training data requirements for machine learning involves both art and science. While rules of thumb provide a starting guideline, practical experimentation and an understanding of the underlying factors allows converging onto reliable estimates. So as a data scientist, be sure to take these key considerations into account while strategizing your data collection and modeling efforts.