# How to apply previously trained ML models to a new dataset¶

When you train an ML model to classify a label on a given dataset using the TrainMLModel instruction, the optimal ML settings (a trained model, encoding, and optionally preprocessing) for each label are exported. These ML setting configurations can subsequently be used to predict that same label on a new dataset for which the true labels are not known. This is done using the MLApplication instruction. This instruction will output a table with the predictions on the new dataset, and the probabilities that these predictions were based on.

Note that the exported ML settings include encodings and preprocessing steps, which are considered hyperparameters inside immuneML. It is thus not possible to apply the ML model on data that was encoded or preprocessed in a different way.

One should also be aware that the way the data was generated or preprocessed before being used inside immuneML can also have an effect on the results. In other words, if there are major differences in how the datasets were generated, for example if the data was sequenced using a different platform, then the predictions of the ML model may not be as correct on the new dataset as they were on the original test dataset.

For a tutorial on training ML models, see: How to train and assess a receptor or repertoire-level ML classifier

For a tutorial on importing datasets to immuneML (for training or applying an ML model on the dataset), see How to import data into immuneML.

## YAML specification example using the MLApplication instruction¶

The MLApplication instruction takes in a dataset and a config_path. The config_path should point at one of the .zip files exported by the previously run TrainMLModel instruction. They can be found in the sub-folder instruction_name/optimal_label_name in the results folder.

definitions:
datasets:
# imported dataset on which the ML model will be applied
my_dataset: # user-defined dataset name
format: AIRR
params: