2022da Given the dataset with two features x1, x2 and the class labels (+,-): A(10, 10,...

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2022da Given the dataset with two features x1, x2 and the class labels (+,-): A(10, 10, +), B( 40, 10, +), C(10, 40, +), D(30,30,-). Illustrate the first 2 iterations of Ada Boost ensemble Algorithm using 3 decision tree classifiers S1, S2, S3. The associated decision stumps for positive class are respectively given as: 51(x1 20) 52(x1 < 30) +;53(x1 50) + [5M] a04664 the 19/01 2022 204664-85 Teddy 2022da Given the dataset with two features x1, x2 and the class labels (+,-): A(10, 10, +), B( 40, 10, +), C(10, 40, +), D(30,30,-). Illustrate the first 2 iterations of Ada Boost ensemble Algorithm using 3 decision tree classifiers S1, S2, S3. The associated decision stumps for positive class are respectively given as: 51(x1 20) 52(x1 < 30) +;53(x1 50) + [5M] a04664 the 19/01 2022 204664-85 Teddy

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AdaBoost Adaptive Boosting is an ensemble learning algorithm used for classification tasks In AdaBoost we create a strong classifier by combining mult... View the full answer