
AI-basic 2
Quiz
•
Information Technology (IT)
•
University
•
Practice Problem
•
Hard
Dinh Hieu
FREE Resource
Enhance your content in a minute
40 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Supervised Learning involves:
Training models only on unlabeled data
Using labeled examples to learn a function or classifier
Guessing class labels without training
Ignoring training data
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A typical Supervised Learning dataset includes:
Records with attributes and known class labels
Only unlabeled raw data
No attributes, just random numbers
Class labels hidden from the learning algorithm
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Decision Tree Induction:
Creates a tree by splitting data based on attribute values
Requires no attributes
Cannot be pruned once grown
Ignores impurity measures entirely
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
An overfitted decision tree:
Generalizes well to new, unseen data
Fits training data too closely, harming generalization
Is always the best model
Occurs only when trees have no leaves
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Precision and Recall measure:
The same quantity in classification performance
Different aspects of predictive accuracy (true positives and coverage of actual positives)
Only false negatives
Irrelevant metrics in model evaluation
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Cross-Validation:
Uses multiple train/test splits to better estimate model performance
Trains on the entire dataset without testing
Is never used in practice
Is identical to using a holdout set
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Support Vector Machines (SVM):
Find a hyperplane that separates classes with the maximum margin
Never use kernels
Do not allow for soft margins
Are only applicable to linear problems
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