The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using "new" examples from the held-out datasets (validation and test datasets) to estimate the model's accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,- A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%

Explanation
Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).
Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform over raw text.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure

Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Example: Regression Model: A Boosted Decision Tree algorithm was used to create and train the model for predicting the repayment rate.
Reference:
https://gallery.azure.ai/Experiment/Student-Loan-Repayment-Rate-Prediction

Explanation:

Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing