Gradient boosting builds trees sequentially, each one correcting the errors of the previous ones by following the gradient of the loss. The predictions are summed, weighted by a learning rate. Very effective on tabular data, it requires careful tuning to avoid overfitting.
Example
HistGradientBoostingClassifier(learning_rate=0.1)