Framing

Question 1

In general, what is supervised learning ?

Answer 1

we combine multiple inputs, to build models, to predict outputs on previously unseen data.

Question 2

What is a label ?

Answer 2

The value we’re predicting

Question 3

What is a feature ?

Answer 3

The inputs values describing the data

Question 4

How is a label represented mathematically ?

Answer 4

y

Question 5

How is a feature represented mathematically ?

Answer 5

{x1, x2, x3,…xn}

Question 6

What is an “labeled example” ?

Answer 6

one piece of data with label, i.e. (x,y)

Question 7

what are labeled example used for ?

Answer 7

to train the model

Question 8

What is an unlabeled example ?

Answer 8

piece of data without labels (x, ?)

Question 9

What are unlabeled examples used for ?

Answer 9

make predictions on new data

Question 10

What is a model ?

Answer 10

The state machine that is performing the predicting

Question 11

What’s the difference between a regression model and and classification model ?

Answer 11

regression models predict continuous values while classification models predict discrete values