adaboost is for classifications stumps are weak learners AdaBoost (Adaptive Boosting) is a popular ensemble learning technique that combines the outputs of several weak classifiers to create a strong classifier. Developed by Yoav Freund and Robert Schapire in 1996, AdaBoost works by iteratively training weak learners, typically decision stumps (simple decision trees with one split),…
XGBoost (eXtreme Gradient Boosting) is a scalable and efficient implementation of gradient boosting for supervised learning. It has gained popularity for its performance and speed, often winning machine learning competitions. Here are the key concepts and features of XGBoost: Key Concepts: Key Parameters: Example in Python: Here’s a basic example of using XGBoost in Python…
we have this code that i put Description The provided code performs a simple linear regression to find the slope (alpha_hat) and intercept (beta_hat) of the best-fit line for the given input_data and output_data. Here’s a step-by-step explanation of the code: Improvements Here is the improved version of the code: Summary of Improvements To multiply…