The Vc Dimension of a Very Flexible Classifier - Statistics Assignment Help

Assignment Task

Task
(a) You are given the following confusion matrix that describes the results of classifying a test set (the columns represent the predicted values and the rows represent the actual values):
predicted classes orange banana apple

• orange
• actual banana 
• apple

For each of the following statements, indicate whether it is correct or not:
1: The accuracy is (1+1+0) / (1+0+10+10+1+0+9+0+0).
2: The precision for class ‘orange’ is 1/ (1+10+9).
3: The recall for class ‘banana’ is 1.
4: The test data used in our experiment consists of 20 oranges.
5: The classifier we have used misclassifies all ‘apples’ as ‘oranges’.

(b) For each of the following statements about classification, indicate whether it is correct or not:
6: The training error of the 1-Nearest Neighbor classifier is always 0.
7: The VC dimension of a very flexible classifier (e.g., neural network) is higher than the VC dimension of a very rigid classifier (e.g., perceptron).
8: AUPRC summarizes the trade-off between recall and precision of a classifier for different thresholds related to the classifier’s parameters.
9: For certain base learners and datasets, the test error of Adaboost may keep decreasing even if the training error becomes zero.
10: One difference between bagging and boosting is that bagging is always performed sequentially, while boosting can also be performed in a parallelized fashion.

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