advanced learning algorithms coursera week 4 answers
Practice quiz: Decision trees
1. Based on the decision tree shown in the lecture, if an animal has floppy ears, a round face shape and has whiskers, does the model predict that it's a cat or not a cat?
- cat
- Not a cat
2. Take a decision tree learning to classify between spam and non-spam email. There are 20 training examples at the root note, comprising 10 spam and 10 non-spam emails. If the algorithm can choose from among four features, resulting in four corresponding splits, which would it choose (i.e., which has highest purity)?
- Left split: 10 of 10 emails are spam. Right split: 0 of 10 emails are spam.
- Left split: 7 of 8 emails are spam. Right split: 3 of 12 emails are spam.
- Left split: 2 of 2 emails are spam. Right split: 8 of 18 emails are spam.
- Left split: 5 of 10 emails are spam. Right split: 5 of 10 emails are spam.
Practice quiz: Decision tree learning
3. Recall that entropy was defined in lecture as H(p_1) = - p_1 log_2(p_1) - p_0 log_2(p_0), where p_1 is the fraction of positive examples and p_0 the fraction of negative examples.
At a given node of a decision tree, , 6 of 10 examples are cats and 4 of 10 are not cats. Which expression calculates the entropy H(p1) of this group of 10 animals?
4. Recall that information was defined as follows: H(p
1
root
)−(w
left
H(p
1
left
)+w
right
H(p
1
right
))
Before a split, the entropy of a group of 5 cats and 5 non-cats is 5
/
10
)
H(5/10). After splitting on a particular feature, a group of 7 animals (4 of which are cats) has an entropy of
H(4/7). The other group of 3 animals (1 is a cat) and has an entropy of H(1/3). What is the expression for information gain?
5. To represent 3 possible values for the ear shape, you can define 3 features for ear shape: pointy ears, floppy ears, oval ears. For an animal whose ears are not pointy, not floppy, but are oval, how can you represent this information as a feature vector?
- [1, 1, 0]
- [1,0,0]
- [0, 0, 1]
- [0, 1, 0]
6. For a continuous valued feature (such as weight of the animal), there are 10 animals in the dataset. According to the lecture, what is the recommended way to find the best split for that feature?
- Use a one-hot encoding to turn the feature into a discrete feature vector of 0’s and 1’s, then apply the algorithm we had discussed for discrete features.
- Try every value spaced at regular intervals (e.g., 8, 8.5, 9, 9.5, 10, etc.) and find the split that gives the highest information gain.
- Choose the 9 mid-points between the 10 examples as possible splits, and find the split that gives the highest information gain.
- Use gradient descent to find the value of the split threshold that gives the highest information gain.
7. Which of these are commonly used criteria to decide to stop splitting? (Choose two.)
- When a node is 50% one class and 50% another class (highest possible value of entropy)
- When the information gain from additional splits is too large
- When the tree has reached a maximum depth
- When the number of examples in a node is below a threshold
Practice quiz: Tree ensembles
8. For the random forest, how do you build each individual tree so that they are not all identical to each other?
- Train the algorithm multiple times on the same training set. This will naturally result in different trees.
- A: Sample the training data with replacement and select a random subset of features to build each tree
- If you are training B trees, train each one on 1/B of the training set, so each tree is trained on a distinct set of examples.
- Sample the training data without replacement
9. You are choosing between a decision tree and a neural network for a classification task where the input x is a 100x100 resolution image. Which would you choose?
- A decision tree, because the input is unstructured and decision trees typically work better with unstructured data.
- A neural network, because the input is structured data and neural networks typically work better with structured data.
- A decision tree, because the input is structured data and decision trees typically work better with structured data.
- A neural network, because the input is unstructured data and neural networks typically work better with unstructured data.
10. What does sampling with replacement refer to?
- Drawing a sequence of examples where, when picking the next example, first replacing all previously drawn examples into the set we are picking from.
- Drawing a sequence of examples where, when picking the next example, first remove all previously drawn examples from the set we are picking from.
- It refers to a process of making an identical copy of the training set.
- It refers to using a new sample of data that we use to permanently overwrite (that is, to replace) the original data.
