How Much Training Data is Enough for Machine Learning Algorithms?

Training data is critical in machine learning as it helps machines to learn and make the predictions. A typical example is a program that identifies and filters spam email.

The quality and quantity of training data determines the accuracy and performance of machine learning models. Therefore, if the quality of machine learning training data sets is not good or accurate, the model will never give precise results, affecting the overall performance of the model which is not suitable to use in real-life. Just like quality, the quantity of machine learning training data set is another key factor while developing an machine learning model. The larger the training data size for the machine learning model, the easier it recognizes diverse types of objects when used in real-life predictions.

the importance of training data in any AI model and machine learning project
Quality and quantity of training data are some of key factors to the success of any AI model and machine learning project.

Is huge quantity of training data truly good?

As lack of data availability will affect your model prediction accuracy while more than enough data can give the best results, but massive datasets require deep learning or a more complex way to feed such data into algorithms. That is why the next important factor you can consider while developing a machine learning model is choosing the right quantity of data sets. Let's get back to the main question.

“How much training is enough for your machine learning model?”

And the answer is “It depends”.

Firstly, it depends on the Complexity of the Problem: the unknown underlying function that best relates relates your input variables to the output variable as per the ML model type.

Secondly, it depends on the complexity of the learning algorithm, nominally the algorithm used to inductively learn the unknown underlying mapping function from specific examples.

It is not a satisfying answer, right? But so far, no one can tell you how much data required for your model.

However, there are other great ways that can help you figure it out.

  1. Search for similar models

    Search for results of applied machine learning problems that are similar to yours. Such studies might inform you how much data you require to use a specific algorithm or perhaps, you can simply average over multiple studies. Google Scholar, and Arxiv are two reliable pages to search for papers.

  2. Use statistical heuristic methods to calculate a suitable sample size.

    In the factor of the number of classes, there must be X independent examples for each class, where x could be tens, hundreds, or thousands depending upon your parameter range. While input features there must be X% more examples than their input features and in model parameters, there must be independent examples for each parameter in the model.

  3. Design a study that evaluates model skill versus the size of the training dataset.

    Plotting the result of your model prediction, as a line plot with training dataset size on the x-axis and model skill on the y-axis that will shed light on how much the quantity of data affects the skill of the model while solving a specific problem with machine learning. A learning curve illustrated in this graph will give you an idea of the data size required to develop a skillful model or perhaps how small data you needed before touching an inflection point of diminishing returns. So, you can perform the study with available data and single performing algorithms like random forest and suggest you develop robust models in the context of a well-rounded understanding of the problems.


The quality and quantity of training dataset is one of the critical criteria to success of machine learning models. However, it is difficult to determine how much training data is sufficient for machine learning model development, but it is clear now “the more the better”. Hence, if you can gather as much data and utilize the information it would be better for you, but waiting for big data acquisition for a long time can delay your projects.


Kotwel is a reliable data service provider, offering custom AI solutions and high-quality AI training data for companies worldwide. Data services at Kotwel include data collection, data annotation and data validation that help get more out of your algorithms by generating, labeling and validating unique and high-quality training data, specifically tailored to your needs.