Please answer the 2 questions below. These questions are in regards to data mini
ID: 3675289 • Letter: P
Question
Please answer the 2 questions below. These questions are in regards to data mining.
Assume we have two models: Model M1: accuracy=85%. tested on 30 instances Model M2: accuracy=75%. tested on 5000 instances Which of the following statement is true? M1 is always more accurate than M2. M1 is always less accurate than M2. M1 is sometimes less accurate than M2. None of the above. Which of the following statements is true? Bagging improves accuracy by taking the sign of the sum of predicted values of each round. Same data item will only appear once in a round in bagging. Different weight is assigned to each data item in bagging. In bagging, if we have 100 samples, then we will do 99 rounds.Explanation / Answer
18. M1 is sometimes less accurate than M2, because sample sapce for M1 is very less than the sample sapce of M2.
19.
Bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
so, Bagging improves accuracy by taking the sign of the sum of predicated values of each round