How many types of datasets are mentioned for ML models in AI Center?

The correct answer reflects the classification of datasets in the AI Center, which identifies three distinct types crucial for the development and training of machine learning models. Understanding these three types is fundamental for any practitioner working with machine learning in the UiPath AI framework.

The three datasets typically referenced are:

1. Training Dataset: This is the primary dataset used to train the machine learning model. It contains input data along with the corresponding labels or outcomes that the model needs to learn from.

2. Validation Dataset: After training, this dataset is used to tune the model and make adjustments to improve its performance without introducing bias. It helps to ensure that the model generalizes well to new, unseen data.

3. Test Dataset: This dataset is employed to evaluate the final performance of the model, providing an unbiased assessment of how well the model is likely to perform in real-world applications.

The distinction between these datasets is essential in the machine learning lifecycle, as each serves a specific purpose that contributes to building robust models capable of making accurate predictions. Understanding the roles of these datasets allows practitioners to effectively design experiments and evaluate their models appropriately.