Basics Flashcards
1
Q
Regression
A
Continuous, Supervised
2
Q
Classification
A
Discrete, Supervised
3
Q
Clustering
A
Discrete, Unsupervised
4
Q
Dimension Reduction
A
Continuous, Unsupervised
5
Q
kNN
A
Memorizes training set, doesn’t learn.
6
Q
Bias
A
Error from erroneous assumprtion in the learning algorithm. From simple and underfitted models
7
Q
Variance
A
Error from sensitivity to small fluctuations in the training dataset. From complex and overfitted models
8
Q
Bias-variance tradeoff
A
Balance between bias and variance to minimise total error
9
Q
A
