Training data is the key input for ML algorithms. Having the right quality and quantity of data is very important to get more accurate results. The larger the training data available with you, more accurate the output of ML algorithms when used for real-life predictions. But the question now is; how will you decide how much training data (quantity) is enough for your ML algorithm. As insufficient data will affect your model prediction accuracy, where more than enough data can give you the best results. But how will you manage to arrange huge quantity of data (or big data) which you can feed into such ML algorithms?
However, many factors decide how much training data is needed for your ML, likewise your model complexity, data for training or validation process. And in some cases, how much data is required to demonstrate that one model is better than another. All these factors considered while choosing the right number of datasets. The quality and quantity of training data are some of the most important factors ML engineers or data scientists are taking into serious consideration while developing a model. However, in coming years it would become more clear how much training data is sufficient for ML model development but it is clear now that “the more data is better”. Hence, if you can acquire 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. Now let us discuss in detail to find how much data is enough for ML algorithms…
1) Nonlinear Algorithms Need More Data
The more powerful machine learning algorithms are often referred to as nonlinear algorithms. Such algorithms are able to learn complex nonlinear relationships between input and output features. These algorithms are often more flexible and non-parametric likewise they can figure out how many parameters are required to model your problem in addition to the values of those parameters. If a linear algorithm achieves good performance with hundreds of examples per class, you may need thousands of examples per class for a nonlinear algorithm, like random forest, or an artificial neural network. The predictions with such models vary based on the particular data used to train them resulting in lots of data required for such model training.
2) Don’t Wait for More Data, Get Started what you have
Get the data what you have and see how effective models are on your problem. You don’t need to get a sufficient amount of training data for your ML and waiting to acquire such data for long days is not a sensible decision. Learn something, then take action to better understand what you have with further analysis, extend the data you have with augmentation, or gather more data from your domain.
3) Using the Statistical Heuristic Rule
There are statistical heuristic methods available that allow you to calculate a suitable sample size. Many of the heuristics are generally used for classification problems as a function of the number of classes, number of input features or number of model parameters.
4) Choose data set depending on the Complexity of Problem and ML Algorithm
“Complexity of the problem” means the unknown underlying function that relates to your variable inputs to the output variable as per the ML model type. Complexity of ML algorith” means the algorithm that is used to inductively learn the unknown underlying mapping function from specific examples to make the best use of training data and integrate the same into the ML model.
5) Model Skill vs Data Size Evaluation
While choosing the training data set for ML, you can design s study that can evaluate the model skill required against the size of the training dataset. You can use a learning curve in which you will be able to project the amount of data required to develop a skillful model or perhaps how small data you needed before touching an inflection point of diminishing returns. To perform this study, plot 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 give you an idea of how much the quantity of data affects the skill of the model while solving a specific problem with ML. 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.