Machine Learning Flashcards
1
Q
What is accuracy?
A
2
Q
Describe AdaBoost
A
3
Q
What is adjusted R^2
A
4
Q
What is agglomerative clustering?
A
5
Q
What is AIC?
A
6
Q
Describe the “almost everywhere” phenomenon
A
7
Q
What is Alpha in Ridge Regression?
A
8
Q
What is Anscombe’s Quartet?
A
9
Q
Describe the architecture of a neural network
A
10
Q
Describe Area under the Curve
A
11
Q
How can you avoid over-fitting a model?
A
12
Q
What is back-proprogation and how does it work?
A
13
Q
Describe bag-of-words
A
14
Q
Compare bagging versus dropout
A
15
Q
What is bagging?
A
16
Q
Describe the basics of deep learning
A
17
Q
Define Bayes’ error
A
18
Q
Describe Bayes’ theorem mathematically
A
19
Q
What are the pros and cons of Bayes’ theorem?
A
20
Q
What is bias?
A
21
Q
What is the bias-variance tradeoff?
A
22
Q
What is Big-O notation?
A
23
Q
What is boosting?
A
24
Q
What is bootstrapping?
A
25
Describe the brierscore
26
What is capacity in a ML context?
27
What is a categorical feature?
28
What is the chain rule in calculus?
29
How is Chi-squared used in feature selection?
30
What is Chi-squared?
31
Describe classification
32
Describe how you can combine items
33
What are some common optimizers for neural networks?
34
What are some of the common output layer functions in ML?
35
What are concave and convex functions?
36
Describe conditional probability
37
What is conditioning?
