Gradient Boosting Machines (GBMs) are a type of machine learning algorithm used for both regression and classification problems. They are a form of ensemble learning, which means that they combine the predictions of several individual models to produce a final prediction. GBMs can handle missing data and outliers.

Here is how GBMs work in detail:

**Decision trees**: GBMs are based on decision trees, which are a type of model that consists of a set of binary decisions that split the input data into smaller subsets based on the values of input features. Decision trees are useful for identifying complex patterns in the data and can handle both categorical and continuous input features.**Gradient boosting**: GBMs use an iterative approach to improve the performance of decision trees. In each iteration, a new decision tree is trained to predict the errors of the previous tree. The algorithm then combines the predictions of all the decision trees to produce a final prediction.**Gradient descent**: GBMs use a technique called gradient descent to optimize the parameters of each decision tree. Gradient descent is an optimization algorithm that iteratively adjusts the parameters of the model to minimize a cost function, which measures the difference between the predicted and actual values.**Regularization**: GBMs can suffer from overfitting, which occurs when the model is too complex and performs well on the training data but poorly on the test data. To prevent overfitting, GBMs use regularization techniques such as shrinkage and feature subsampling.**Hyperparameter tuning:**GBMs have several hyperparameters that can be tuned to optimize the model’s performance, such as the learning rate, the number of decision trees, and the maximum depth of each tree. Hyperparameter tuning is an important step in the model-building process and can significantly improve the model’s performance.

Overall, Gradient Boosting Machines are a powerful and flexible machine learning algorithm that can handle a wide range of regression and classification problems. However, they can be computationally expensive and require careful tuning of hyperparameters to achieve optimal performance.