Deciding the quantity of training data set for AI or machine learning model is one the most challenging factor for the engineers. Actually training data is the key input to AI development, and having the right quality and quantity of data sets is important to get accurate results. The larger the training data available for the algorithm, it will help model to perceive the diverse types of objects making easier to recognize when used in real-life predictions.
Factors Considered While Choosing the Quantity of AI Training Data:
- Depends on the Complexities of Problem and Learning Algorithms
- Model Skill vs Data Size Evaluation
- More Data Required for Nonlinear Algorithms
Actually, there are various factors that decide the quantity of training data for AI models. Depending on complexities of problem and machine learning algorithms you need training data sets. As much as data is used to train the AI model, the accuracy would be higher in various scenarios.
Similarly, while choosing the training data set for machine learning you can design s study that can evaluate model skill required against the size of training dataset. So, you can perform the study with available data and single performing algorithms like random forest and suggest you to develop a robust model in the context of well-rounded understanding of the problems.
While on the other hand, nonlinear algorithms are usually known as one the most powerful machine learning algorithms. As they are capable to learn the complex nonlinear relationships between inputs and output features. If you are using the nonlinear algorithms you need adequate amount of data sets and need to hire machine learning engineer that can work with such applied mathematics.
Such algorithms are often more flexible and even nonparametric means they can find out itself how many parameters are required to model your problem in addition to the values of those parameters. The predictions with such models vary based on the particular data used to train them resulting lots of data required for such model training.
Don’t Wait for More Data, Get Started what you have
It is not necessary you will get sufficient amount of training data for your ML and waiting to acquire such data for long days is not a sensible decision. Don’t let the problem of the training set size stop you from getting started on your model prediction problem solving.
Get started with the data you can, use what you have, and check how effective models are on you problem. Acquire something then take action to understand better what you have with for further analysis and then increase the data you have with augmentation or collect more data from your domain to make your model training more accurate.