Machine Learning is used applied when the scale of data is too large for rule-based systems to handle. For example, you might be able to manually predict purchasing pattern at a small roadside shop by going through the sales entries but it would be difficult to do the same for a large departmental store simply because the number of factors involved are very high.
So, it is important to collect, clean, and prepare data to make it suitable for consumption by ML model training algorithms. It is also important to analyze the data to run sanity checks to validate the quality of the data and to understand the data. Now, let’s understand Zuci’s approach to testing machine learning applications and how ZUJYA, our test automation platform helps.
At Zuci, we separate data into training and testing sets as part of validating machine learning applications. To understand our approach better, let’s look at a visual representation of ZUJYA’s design to validate machine learning models.
Separating data into training and testing sets is important part of evaluating machine learning models. When we separate a data set into a training set and testing set, most of the data is used for training, and a smaller portion of the data is used for testing.
Subsequently, as part of data analysis, the need to randomly sample the data exists to help ensure that the testing and training sets are similar. By using similar data for training and testing, we minimize the effects of data discrepancies and better understand the characteristics of the model
Now, after a model has been processed by using the training set, you test the model by making predictions against the test set. Because the data in the testing set already contains known values for the attribute that you want to predict, it is easy to determine whether the model’s guesses are correct.
Here is a breakdown of ZUJYA’s key features that helps in testing Machine Learning applications.