Answer: Decision trees, random forests, logistic regression, and XGBoost.

The correct answer is **B) Decision trees, random forests, logistic regression, and XGBoost.** Here's why: - **Decision Trees**: These algorithms split data into smaller subsets based on decision rules, forming a tree structure for predictions. - **Random Forests**: An ensemble technique that constructs multiple decision trees and merges their predictions for enhanced accuracy. - **Logistic Regression**: A statistical approach for estimating probabilities in binary classification scenarios. - **XGBoost**: A scalable and efficient gradient boosted decision trees method known for its high performance. **Key Insights**: - Databricks AutoML intelligently selects and optimizes these algorithms according to your dataset and the nature of the problem. - It assesses model effectiveness using various metrics to identify the top-performing model. - The AutoML experiment results detail the specific algorithms and their configurations. **Why Not the Others?** - **Option A** and **C** miss logistic regression and XGBoost, crucial for classification. - **Option D** includes Prophet and Auto-ARIMA, which are tailored for time series forecasting, not classification tasks.

Author: LeetQuiz Editorial Team