Given a dataset comprising of instances 1 to 5, if the Boosting model trained during iteration 0 only classified 3 instances correctly, what is the updated weight value of each instance that was classified incorrectly? Write your answer correct to one decimal place.

Consider the binary classification task of predicting whether a generator is "Faulty" or "Good". Towards this extend an AdaBoost model was constructed using three models. Given a new generator we want to calculate whether the generator is faulty or good. Is the new generator "Faulty" or "Good", based on the following information?

• When creating the model, we assigned the values -1 to the label "Faulty", and the 1 to the label "Good"
• Each individual model predicted the following values: Model 1: 1, Model 2: 1, Model 3: -1
• The voting weight for each model is as follows: Model 1: 0.25, Model 2: 0.30, Model 3: 0.45

Consider the binary classification task of predicting whether a person has a heart disease based on: whether the patient suffers from chest pain (Chest Pain), the blood pressure of the patient (Blood Pressure) and the age of the patient (Age). For this task, we opted to use a random forest composed of three decision trees. The following diagram illustrates each of the trees:

Assuming that the random forest uses majority voting, what prediction will be made for the following query?

Chest Pain = Yes, Blood Pressure = Low, Age = Below 50

Given a dataset comprising of instances 1 to 5, if the AdaBoosting model trained during iteration 0 only classified 3 instances correctly, what is the voting weight of the model trained during iteration 0? Write your answer correct to one decimal place.

Consider the binary classification task of predicting whether a generator is faulty or good. To deal with the problem posed, we have decided to construct an AdaBoost model. What is the error rate for the model, based on the information provided in the table below?