Once the machine learning algorithm is trained in the training data, the next step is to run the model in the test data.

The entire set of attributes or features of the data is divided into predictor attributes and objective attributes. The predictor attributes/features of the dataset are fed as input to the machine learning model and the model uses these attributes to predict the objective attributes. The test set uses only the predictor attributes. Now, the algorithm uses the predictor attributes and outputs predictions on objective attributes. Once the output is provided, it is compared against the actual data to understand the quality of output from the algorithm.

The results should be properly presented for further analysis. What to present in the results and how to present them are critical. They may also bring to the fore new business problems.