Predicting Iris Dataset Features with CatBoost Model

This custom function loads a pre-trained CatBoost model and uses it to predict new features of the Iris dataset. First, the function loads the model, then it generates a random features array with the same shape as the input features. Finally, it uses the model to predict these random features and returns the predicted results.

Technology Stack : CatBoost, NumPy

Code Type : Custom function

Code Difficulty : Intermediate

                
                    
import numpy as np
import catboost as cb

def predict_iris_features(X, model_path):
    """
    Load a pre-trained CatBoost model and use it to predict features of the Iris dataset.
    """
    # Load the pre-trained model
    model = cb.CatBoostModel.load_model(model_path)
    
    # Generate random features based on the input features
    random_features = np.random.rand(X.shape[0], 10)
    
    # Predict the new features using the loaded model
    predicted_features = model.predict(X)
    
    return predicted_features