What is training data?

Training data are available within the framework of Artificial intelligence and for Machine learning indispensable to train the system. In unsupervised learning, no examples are needed and the AI system can be trained directly with appropriate input data. Supervised learning, on the other hand, requires sample data. For this data, the target variable is given. The data set is called the sample data set.

In supervised learning, the data set is divided into different data sets: training, validation and test data. These three data sets are then created from the "machine learning flat file" (the sample data set). Thus, the possible division is as follows:

  • 70% Training record
  • 10% Test data set
  • 20% Validation record

The Training dataset is a dataset filled with examples. These are also called target variables. The data set is used for learning patterns and correlations. An adjustment of weights of the algorithm is trained via a training data set. The algorithm thus learns from such data. The training data with the corresponding examples are then used for Regression and classification problems needed. Algorithms tend to over-adapt to learned patterns from the training data. Interrelationships and relationships can then be internalised too much from the training data and as a consequence these rules no longer function with a high degree of accuracy in their entirety.

Test data are independent of training data and should have the same probability distribution as the training data. During training, the test data is not used and the algorithm does not know such data. With the test data, examples and target variables are available and the corresponding quality of the model can then be measured. As soon as the trained model seems to fit the test data correctly and the example data are predicted in a good quality, the model is applied to unknown data to be evaluated.

The Validation data set can also be regarded as an example data set. Such data is used for a Tuning with hyperparameters of a model. Above all, the overfitting of the model to training data is to be avoided.

Why do you need training data?

In general, training data is needed, to set up machine learning and artificial intelligence correctly. The training of systems is supported with requirement-specific training data sets. The required data sets can be newly and individually provided, the data is subjected to labelling and Annotation. Existing training data and system results are also validated.

One of the most difficult tasks in the development of a machine learning system is the Collecting large amounts of high quality AI training data. Service providers offer unique and newly created AI training data for each of your projects. Thus, photos, audio and video recordings and also texts are supplied and these then support the programming of learning-based algorithms.

What training data do artificial intelligence and machine learning need?

Artificial intelligence is used in route planning, in quality controls in production and in the analysis of X-ray images. Training data for machine learning in particular is becoming increasingly important.

AI systems are trained with suitable data. The patterns recognised in the training data and the information can then be transferred by the systems to unknown data sets after the training process is complete. The need for such training data will increase greatly in the years ahead.

For companies that develop or also use AI, frequently also Records with personal data referenced. Legal requirements must always be observed and complied with when working with training data in machine learning systems. It is the case that data sovereignty and data care must replace data thriftiness as the guiding principle in order to be able to meet the major challenges of the future.