Machine learning algorithms can be less accurate and powerful when working with small datasets because their ability to recognize patterns is generally proportional to the dataset's size. How much data is enough for Machine learning? What are the ways to use Machine learning when we have very limited data set?
The more complicated data you have the more data you need.
Also, if we talk about neural networks, the more neurons you have the less data you might need, that is kinda inverse behaviour.
So we cannot name amount in numbers, it is highly related to the problem you are trying to solve. If you know that data is linear, even 2 values might be enough. If data is highly non-linear, sometimes even 2 000 data points are not enough. If we are working with images, you might even need 50 000 or more. Also, some machine learning methods work better with low amount of data, others - with large datasets. In general, the more data, the better model we can make.
I used a dataset with only 200 rows to conduct gradient boosting decision tree models. The importance is not the size. However, you need to tune parameters, cross-validate your models, or use other techniques to reduce the unbalanced representation in your dataset. Regardless of the size of your dataset, the most important is the decision you take and how you justify it interpreting outcomes evidenced by validity metrics measures.
Machine learning can indeed work with small datasets. There are techniques and strategies to help models learn effective patterns from limited data. Here are two points to consider:
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