How do machine learning algorithms require load training data?

Training data is the key input for machine learning (ML) and it is important to have the right quality and quantity of the data set to get accurate results. The larger the training data available to the ML algorithm, the better it will help the model to understand different types of objects, making them easier to identify when used in real-life predictions.

But the question here is how will you decide how much training is enough for your machine learning. Since insufficient data will affect the prediction accuracy of your model while more than enough data can give best results, but can you manage large data or large amount of datasets and require deep learning or feeding such data into algorithms? It also requires a more complex method.

Many factors decide how much training data is needed for machine learning such as the complexity of your model, machine learning algorithms and data training or validation process. And in some cases, how much data is needed to demonstrate that one model is better than another. All these factors are considered while choosing the right number of datasets, let us discuss them in more detail to find out how much data is sufficient for ML.

Depends on the complexity of the problem and the learning algorithm
One of the most important factors when selecting training data for machine learning is the complexity of the problem which means the unknown underlying function that relates your variable input to the output variable according to the ML model type.

Also read: What are the different types of data sets used in machine learning?

Similarly, the complexity of the machine learning model algorithm is another important factor when selecting the right amount of data sets. The algorithm is used to learn the unknown underlying mapping function from specific examples in order to make best use of the training data and integrate it into the machine learning model.

Using the Statistical Heuristic Rule
In statistical terms, several components are considered such as a factor of the number of classes, a factor of the number of input features, and a factor of the number of model parameters. And there are statistical heuristic methods available that allow you to calculate the appropriate sample size.

To factor in the number of classes, there should be X independent instances for each class, where x can be tens, hundreds or thousands depending on your parameter range. Whereas the input features should have X% more examples than their input features and model parameters, the model should have independent examples for each parameter.

Model Skills vs. Data Size Evaluation

When selecting a training data set for machine learning you can design a study that evaluates the required model skills against the size of the training dataset. To perform this study, plot your model’s prediction result as a line plot with the training dataset size on the x-axis and the model skill on the y-axis, giving you an idea of ​​how much data the skill needs. How much of a model is affected by solving a specific problem with machine learning.

You can use a learning curve in which you will be able to project the amount of data needed to develop an efficient model or perhaps how little data you need before the inflection point of diminishing returns is touched. Therefore, you can study with available data and single performing algorithms like Random Forest and suggest that you develop robust models in terms of well-understood problems.

More data needed for nonlinear algorithm

Nonlinear algorithms are generally known as the most powerful machine learning algorithms. Since they are capable of learning complex non-linear relationships between input and output features. If you are using nonlinear algorithms then you need a substantial amount of data sets and need to hire a machine learning engineer who can work with this kind of applied math.

Also read: How to prepare training data for machine learning?

Such algorithms are often more flexible and even non-parametric means they themselves can figure out how many parameters are needed to model your problem in addition to the values ​​of those parameters. The predictions of such models vary depending on the particular data used to train them which results in the need for a lot of data for training such models.

Don’t wait for more data, start with what you have
You do not need to acquire a substantial amount of training data for your ML and waiting to receive such data for long days is not a wise decision. Don’t let the problem of training set size prevent you from getting started on your model prediction problem-solving.

Start with the data you can, use what you have and check how effective the models are on your problem. get some then take action to better understand what you have for further analysis and then augment the data from your domain to make your model training more accurate