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pruefungen:nebenfach:bwl_bin20 [11.08.2020 15:01] – angelegt nakami | pruefungen:nebenfach:bwl_bin20 [11.08.2020 15:07] – fix everything! nakami | ||
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- | Braindump SS 2020 | + | ====== |
- | entnommen | + | Entnommen |
+ | |||
+ | Zeit: 90min; wirkt ausreichend, | ||
+ | Anzahl Seiten: 18(!) | ||
+ | |||
+ | == 1. Preprocessing == | ||
+ | |||
+ | |||
+ | Given dataset with 5,050 examples, shall predict whether students pass exam or not | ||
- | 1. Preprocessing: | ||
- | given dataset with 5,050 examples, shall predict whether students pass exam or not | ||
a) explain what preprocessing is necessary for using a nueral network on that data set (conversion into numeric, missing values, etc) | a) explain what preprocessing is necessary for using a nueral network on that data set (conversion into numeric, missing values, etc) | ||
- | b) I. What is the problem with a data set that has label " | + | |
+ | b) | ||
+ | |||
+ | I. What is the problem with a data set that has label " | ||
II. Is accuracy a good metric here? | II. Is accuracy a good metric here? | ||
- | -> (no, | + | |
+ | Solution: | ||
c) Your boss wants to do the following tasks: | c) Your boss wants to do the following tasks: | ||
+ | |||
1. Calculate the profit in the future | 1. Calculate the profit in the future | ||
+ | |||
2. Put employees in pre-definded groups | 2. Put employees in pre-definded groups | ||
+ | |||
+ | Which technology can he use that is able to solve BOTH problems. | ||
+ | |||
(In other wordsName 2 models that can do regression AND classification) | (In other wordsName 2 models that can do regression AND classification) | ||
- | 2. Evaluation | + | |
+ | == 2. Evaluation | ||
Confusion matrix about people spending a lot in an online shop. | Confusion matrix about people spending a lot in an online shop. | ||
+ | |||
a) calculate precision, recall and F1 score | a) calculate precision, recall and F1 score | ||
+ | |||
b) argue what should one choose to classify customers that will likely review my product positively, so that I will send them my product for free (precision!) | b) argue what should one choose to classify customers that will likely review my product positively, so that I will send them my product for free (precision!) | ||
+ | |||
c) Your model shows a low training error and a high validation error error. What might be the issue? What can you change to fix it? | c) Your model shows a low training error and a high validation error error. What might be the issue? What can you change to fix it? | ||
- | -> overfitting | + | |
+ | Solution: | ||
d) given 3 ROCs, which is better | d) given 3 ROCs, which is better | ||
- | 3. Decision Trees | + | |
+ | == 3. Decision Trees == | ||
a) read classification from a given tree: What two groups are targeted? | a) read classification from a given tree: What two groups are targeted? | ||
(Decision tree has leafs "Will respond to campaign" | (Decision tree has leafs "Will respond to campaign" | ||
+ | |||
b) what to do if tree is too complex | b) what to do if tree is too complex | ||
+ | |||
c) given two trees, which is better | c) given two trees, which is better | ||
- | 4.Neural Networks | + | |
+ | == 4. Neural Networks | ||
Given a Neural Network | Given a Neural Network | ||
+ | |||
a) compute activation potential and activation value | a) compute activation potential and activation value | ||
+ | |||
b) calculate error signal and new weight | b) calculate error signal and new weight | ||
+ | |||
c) explain back propagation | c) explain back propagation | ||
+ | |||
d) explain black box property | d) explain black box property | ||
- | 5. SVMs | + | |
+ | == 5. SVMs == | ||
+ | |||
a) what happens if point x (= one support vector) is erased from data set | a) what happens if point x (= one support vector) is erased from data set | ||
- | 6. Social Media Mining | + | |
+ | == 6. Social Media Mining | ||
+ | |||
a) What is the difference between Social Media Mining and Social Media Analytics? | a) What is the difference between Social Media Mining and Social Media Analytics? | ||
+ | |||
b) see two WoM values, explain which shows better result of a marketing campaign | b) see two WoM values, explain which shows better result of a marketing campaign | ||
+ | |||
(it's not mentioned whether the marketing campaign is intended to result in more direct clicks or clicks through recommendation) | (it's not mentioned whether the marketing campaign is intended to result in more direct clicks or clicks through recommendation) | ||
+ | |||
c) difference between centrality and centralization | c) difference between centrality and centralization | ||
+ | |||
d) calculate closeness centralization of network | d) calculate closeness centralization of network | ||
+ | |||
e) argue which network is better, all centrality and centralization measures (closeness centralization is from the last step) and the actual networks were given | e) argue which network is better, all centrality and centralization measures (closeness centralization is from the last step) and the actual networks were given | ||
- | 7. Association rules | + | |
+ | == 7. Association rules == | ||
10.000 shoes sales were tracked. Left side: Single-shoe-pair occurence (in basket) in percentage. Right side: Two shoe pairings occurence (in basket) in numbers. We are looking at Speedrunner, | 10.000 shoes sales were tracked. Left side: Single-shoe-pair occurence (in basket) in percentage. Right side: Two shoe pairings occurence (in basket) in numbers. We are looking at Speedrunner, | ||
+ | |||
a) Find the four 1-to-1-itemset- association rules ({A} -> {B}) from given data. calculate support & confidence | a) Find the four 1-to-1-itemset- association rules ({A} -> {B}) from given data. calculate support & confidence | ||
b) describe FP-Growth | b) describe FP-Growth |