Have you ever thought about how the prediction of winning a match appears on the screen of the FIFA world cup? Is It due to the dataset the answer is YES!
Collections of samples make datasets. The samples can be images, audio, text, videos, or collections of any of them. Multiple resources are there for data collection although it’s very not easy to process. Google introduced its platform for finding a dataset to perform and check how a particular machine learning model acts.
Normally historical happenings altogether build a collection of data that ML engineers uses in their algorithm. SVM, Naive Bayes, and Random Forest are simple and effective models for multiclass classification.
How to Deal with Small Datasets in Machine Learning
Select Effective & Simple Model
Large models have a lot of parameters and need a lot of data features to train the algorithm. The selection of a machine learning model is very important than datasets. No doubt, the parameters are playing vital roles inside the model but how the specific things work.
Restrict The Space
Are you looking for the best-fit model that works for a given dataset that is small? Properly define the loss function and the class of function for it. Consider an example, limit your function class to only your linear data rather than non-linear. Tune the loss function in a way your model does not overfit to avoid disaster.
Choose Hyperparameters Wisely
It is very difficult to choose which hyperparameters are good for your model to work better so that it performs the best result.
Let us consider the example of image binary classification – distinct options are but CNN is most powerful for images. Using techniques like data acquisition datasets would increase but what to do in another case. Drop out some perceptron so that model could not understand the features. Another is to change the activation layer to some extent that works far better.
Increase Dataset Amount
Repeating from the given data sample is not a good practice for the ML model. However, use the following methods for it.
Data Augmentation: Add some noise, rotate data randomly, and change the axis to produce and generate the dataset.
Transfer Learning: It is a process where a model is trained on one process that helps to train another model for accurate training.
Generative Adversarial Networks: GANs is a deep learning model that is able to produce new data similar to dataset you have.