How Much Training Data is Required for Machine Learning?

In Machine Learning, we train models using training data. In this article, I’ll walk you through how much training data is required for a machine learning model.

How Much Training Data is Required for Machine Learning?

Given the difficulty of observing and collecting the response variable for data instances, you might be wondering how much training data is needed for a machine learning model to be up and running.

Unfortunately, this question is so specific to the problem that it is impossible to give a universal answer or even a rule of thumb.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

Factors Determining How Much Training Data is Required

These factors determine the amount of training data required to train a machine learning model:

  1. The complexity of the problem. Does the relationship between the input features and the target variable follow a simple pattern or is it complex and nonlinear?
  2. Precision requirements. If you only need a 60% success rate for your problem, less training data is needed than if you need to achieve a 95% success rate.
  3. The dimensionality of the functional space. If only two input features are available, less training data will be needed than if there were 2000 features.

A guiding principle to remember is that as the training set grows, the models will become (on average) more accurate. More training data translates to greater accuracy due to the data-driven nature of machine learning models.

Since the relationship between features and target is fully learned from training data, the more you have, the better able the model is to recognize and capture more subtle patterns and relationships.

The image below shows whether the existing sample of 3,333 training instances contains enough data to build an accurate machine learning model. The black line represents the average accuracy over 10 repetitions of the evaluation routine and the shaded bands represent the error bands.

how much training data is required

Conclusion

We can just conclude that the machine learning model won’t improve significantly if you add more training instances. This does not mean that significant improvements could not be made by using more features.

Hope you liked this article on how much training data is required for a machine learning model. Please feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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