Question

In which approach do the classification models train on data sets whose distribution are modified in comparison to the distribution of the original training data set

Answer 1

• Start by creating a tree on training data, where each observation is assigned an equal weight.
• Then compute the predicted classification and weights are redetermined and assign greater weight to those observations that are difficult to classify and lower weights to those that are easy to classify. Weights for all observations must sum to 1.
• Second tree is grown on weighted data (weights are based on residuals or misclassification error). Idea is to improve prediction of first tree.
• Weights are redetermined and assign higher weights if it is classified incorrectly.
• New Model is now Tree 1 + Tree 2
• Compute residuals or classification error from this new 2-tree model (Tree1 + Tree2) and grow 3rd tree to predict revised residuals.
• Then subsequent trees help in classifying observations that are not well classified by preceding trees.
• The final prediction is a weighted sum of the predictions made by previous tree models.

Answer 2

Answer:

In which approach do the classification models train on data sets whose distribution are modified in comparison to the distribution of the original training data set