Type 2 Diabetes(AdaBoost)

AdaBoost, short for Adaptive Boosting, is a machine learning algorithm, formulated by Yoav Freund and Robert Schapire. It is a meta-algorithm, and can be used in conjunction with many other learning algorithms to improve their performance. AdaBoost is adaptive in the sense that subsequent classifiers built are tweaked in favor of those instances misclassified by previous classifiers. AdaBoost is sensitive to noisy data and outliers. Otherwise, it is less susceptible to the overfitting problem than most learning algorithms.The preset modle information and parameters are discribed as below:

• n_estimators：100

The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

• base_estimator：DecisionTreeClassifier(max_depth=1)

The base estimator from which the boosted ensemble is built.

• learning_rate：1

Learning rate shrinks the contribution of each classifier by learning_rate.

• algorithm：SAMME.R

SAMME.R real boosting algorithm.

• random_state：None

The random number generator is the RandomState instance used by np.random.