Chp 4 Components of Learning

Question 1

How is data usually represented

Answer 1

As a matrix

Question 2

Data science spend the most time

Answer 2

cleaning data

Question 3

Target function

Answer 3

A function that maps X to Y, we do not know this function and the goal is to recreate this function

Question 4

Learning job

Answer 4

Create a hypothesis function that also maps X to Y very similarly to the target function

Question 5

Learning steps 5

Answer 5

1. We have an unknown target function F which maps X to Y
2. We have certain training examples and we use those training examples as part of a learning algorithm
3. The learning algorithm has a number of hypotheses.
4. These training examples and hypotheses together will produce a final hypothesis.
5. We hope this final hypothesis is very close to the target function

Question 6

Learner input output

Answer 6

seen data as input, classifier as output

Question 7

Classifier input output

Answer 7

unseen data, response to that data

Question 8

Model is an

Answer 8

artifact, learner builds a model and classifier uses that model to predict

Question 9

Curse of dimensionality

Answer 9

The various challenges and complications that come from data that is very high in dimensions, too much data to handle every single case

Question 10

Generalizing

Answer 10

Being able to adapt to data that the model has not seen before

Question 11

Selection

Answer 11

Selecting the data you need

Question 12

Preprocessing

Answer 12

Clean data and understand what you need to remove

Question 13

Transformation

Answer 13

Transform it into the shape you want, add/remove attributes

Question 14

Data Mining

Answer 14

Get patterns from the data

Question 15

Supervised learning

Answer 15

Model is trained on labeled data, input output pairs. Algorithm learns to map input to output

Question 16

Unsupervised Learning

Answer 16

Model is trained on unlabeled data. Algorithm looks for pattern or structure in data

Question 17

Reinforcement learning

Answer 17

Algorithm receives feedback in form of rewards or penalties. Goal is to maximize reward over time

Question 18

Binarization

Answer 18

Converting continuous or categorical data into binary form

Question 19

Discretization

Answer 19

Converting continuous data into discrete categories or intervals

Question 20

Classification

Answer 20

The task of learning a target function that maps each attribute set x to one of the predefined class labels

Question 21

How do decision trees work?

Answer 21

Takes some data, uses tree induction algorithm to understand this data, learn a model from it, apply deduction to get new responses right

Question 22

Decision trees are always

Answer 22

Binary

Question 23

Hunts Algorithm

Answer 23

Dt set of training records that reach a node t
If Dt contains records that belong to the same class Yt, then t is a leaf node labeled as Yt
If Dt is an empty set, then t is a lead node labeled by the default class Yd
If Dt contains records that belong to more than one class, use an attribute test to split the data into smaller subsets.
Recursively apply the procedure to each subset

Question 24

what is the default class

Answer 24

The class that is most frequent in the data set