AdaBoost assigns equal weights to all examples initially, where the weight is equal to
n- the number of examples.
But then the AdaBoost documentantion includes a hyperparameter
learning_rate defined as:
learning_rate float, default=1.
Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learning_rate and n_estimators parameters.
But then Decision Trees do not use
learning_rate parameter since it not a gradient-based model learning approach. Besides,
AdaBoost already assigns
1/n to each sample. How then the definition of
learning_rate defined in the documentation fits here?