theory lecture 5

Question 1

statistics

Answer 1

more theory-based and top-down ideas. it is more model based and focuses on testing hypotheses.

Question 2

machine learning

Answer 2

more heuristic and focused on improving performance of a learning agent. it also looks at real-time learning and robotics.

Question 3

data mining and knowledge discovery

Answer 3

integrates both theory and heuristics. the focus is on the entire process of knowledge discovery, including data cleaning, learning, and integration and visualisation of results.

Question 4

test data

Answer 4

shows how well the machine is learning after the training in supervised learning systems.

Question 5

regression

Answer 5

a machine learning model where you try to predict a score.

Question 6

association

Answer 6

a type of unsupervised learning where you try to see the data types and how well they associate with each other.

Question 7

clustering

Answer 7

a type of unsupervised learning where you eg. try to differentiate dogs and cats.

Question 8

ANN

Answer 8

the data is split into three subsets for classification; ~60% training, ~20% validation, and ~20% testing. it is a prediction model that is inspired by the way a brain works with neurons. it is what deep learning is based on.

Question 9

overtraining

Answer 9

when you use too much data for training and the algorithm knows everything about the sample, but it may not recognise anything outside of the sample.

Question 10

target variable

Answer 10

the variable we are trying to predict based on the attributes in the columns of a table.

Question 11

dimensionality of a data set

Answer 11

the sum of the dimensions of the features/attributes.

Question 12

curse of dimensionality

Answer 12

when you have too many dimensions and it becomes hard to predict a value.

Question 13

CRISP-DM

Answer 13

a model used to show the knowledge discovery process flow. the process is highly repetitive and experimental. you may have to back in steps, eg. if your model is different in practice.

Question 14

C&RT

Answer 14

a prediction model. it stands for Classification and Regression Trees.

Question 15

Random Forest

Answer 15

a prediction model that combines different trees.

Question 16

Boosted Tree

Answer 16

a prediction model that combines trees in a boosting way.

Question 17

Fusion

Answer 17

a prediction model that combines different algorithms.

Question 18

1-Away

Answer 18

means the accuracy including a prediction of 1 class away. eg. it predicts 4, but it is actually 5.

Question 19

SVM

Answer 19

a prediction model, using a line that divides your data.

Question 20

linear regression

Answer 20

a method used for classification with the formula w0 + w1x + w2y >= 0. it computes w1 from the data to minimise the squared error to ‘fit’ the data. it uses a line to classify data into a class.

Question 21

decision trees

Answer 21

a method for classification that splits data by drawing multiple horizontal and vertical lines.

Question 22

confusion matrix

Answer 22

the primary source for accuracy estimation in classification problems. it shows how confused your model is between two classes. you can put your testing data into the matrix to see how many are correct.

Question 23

precision

Answer 23

given something is positive in a predicted class, how often do you predict it right?

Question 24

recall

Answer 24

given that the true class is positive, how often do you predict it right?

Question 25

decision tree

Answer 25

puts your data in a format to split it up. the higher attributes in the tree are more important.