### CatBoost Basics
In this problem, you will learn the basics of CatBoost, a popular gradient boosting framework. CatBoost is known for its ability to handle categorical features and its high performance on large-scale datasets.
**Example:** Train a CatBoost model on the Iris dataset to classify flowers into different species.
**Constraints:** Use the CatBoost library and NumPy for this problem.
Your task is to implement a function that trains a CatBoost model on the Iris dataset and returns the predicted species.
Test Cases
Test Case 1
Input:
[[5.1,3.5,1.4,0.2],[5.4,3.4,1.7,0.2],[4.6,3.4,1.7,0.2]]Expected:
[0]Test Case 2
Input:
[[6.2,3.4,5.4,2.3],[5.9,3.0,4.2,1.5],[5.6,2.8,4.9,2.0]]Expected:
[1]+ 3 hidden test cases