
Chapter 6 Part 1
Authored by Aiman Dolah
Computers
University
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main advantage of using a Random Forest over a single Decision Tree?
Faster computation
Reduces overfitting and improves accuracy
Requires less data
Needs fewer features
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In a Decision Tree, what does each internal node represent?
Output class
A test on an attribute/feature
The average of predictions
A leaf node
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does “bagging” stand for in ensemble learning?
Boosting aggregate
Bootstrap aggregating
Binary aggregation
Base grouping
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the two main techniques used in building a Random Forest?
K-means and SVM
Cross-validation and scaling
Random sampling of data and random feature selection
Neural nets and normalization
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the output of a classification Random Forest?
Probability
Majority vote of the trees
Average value
Single tree prediction
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does “deep” in Deep Learning refer to?
Large data
Complex math
Many layers in the neural network
Long training time
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which neural network type is best for image recognition?
RNN
CNN (Convolutional Neural Network)
Decision Tree
SVM
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