CS代考 COMP90049 • Machine Learning – cscodehelp代写
Lecture 2: Machine Learning Concepts
Introduction to Machine Learning Semester 2, 2021
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Acknowledgement:
Last lecture
• Housekeeping COMP90049 • Machine Learning
This lecture
• Establishing terminology
• Basic concepts of ML: instances, attributes, learning paradigms, … • Python demo
Basics of ML: Instances, Attributes and Learning Paradigms
Typical Workflow
https://commons.wikimedia.org/wiki/File:CRISP- DM_Process_Diagram.png
Terminology
• The input to a machine learning system consists of:
• Instances: the individual, independent examples of a concept
also known as exemplars
• Attributes: measuring aspects of an instance
also known as features
• Concepts: things that we aim to learn generally in the form of labels or classes
Example: weather.nominal Dataset
Outlook Temperature Humidity Windy Play
sunny overcast rainy rainy rainy
hot high hot high hot high
mild high cool normal cool normal
FALSE TRUE FALSE FALSE FALSE TRUE
no no yes yes yes no
Example: weather.nominal Dataset
sunny overcast rainy rainy rainy
Example: weather.nominal Dataset
sunny overcast rainy rainy rainy
The MNIST digit classification data set
• How many instances do you see in the dataset above?
The MNIST digit classification data set
• How many instances do you see in the dataset above? • What are these instances?
The MNIST digit classification data set
• How many instances do you see in the dataset above?
• What are these instances?
• What features or attributes do we have for the instances?
What’s a Concept?
“Concepts” that we aim to learn:
• Predicting a discrete class (Classification)
• Grouping similar instances into clusters (Clustering)
• Predicting a numeric quantity (Regression)
• Detecting associations between attribute values (Association Learning)
A Word on Supervision
• Supervised methods have prior knowledge of a closed set of classes and set out to discover and categorise new instances according to those classes
• Unsupervised do not have access to an inventory of classes, and instead discover groups of ‘similar’ examples in a given dataset. Two flavors :
A Word on Supervision
• Supervised methods have prior knowledge of a closed set of classes and set out to discover and categorise new instances according to those classes
• Unsupervised do not have access to an inventory of classes, and instead discover groups of ‘similar’ examples in a given dataset. Two flavors :
– dynamically discover the “classes” (implicitly derived from grouping of instances) in the process of categorising the instances [STRONG]
… OR …
– categorise instances as certain labels without the aid of pre-classified data [WEAK]
Classification
• Assigning an instance a discrete class label
• Classification learning is supervised
• Scheme is provided with actual outcome or class
• The learning algorithm is provided with a set of classified training data
• Measure success on “held-out” data for which class labels are known (test data)
Example Predictions for weather.nominal
Outlook sunny sunny overcast rainy rainy rainy overcast sunny sunny rainy sunny overcast overcast rainy
Temperature Humidity hot high hot high hot high mild high
cool normal cool normal cool normal mild high cool normal mild normal mild normal mild high hot normal mild high
Windy FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
True Label
Classified
no yes yes yes no yes no yes yes
yes no yes yes yes yes no yes
Clustering
• Finding groups of items that are similar
• Clustering is unsupervised — the learner operates without a set of
labelled training data
• The class of an example is not known … or at least, not given to the learning algorithm
• Success often measured subjectively; evaluation is problematic
Clustering over weather.nominal
Outlook Temperature Humidity Windy Play
sunny overcast rainy rainy rainy
hot high hot high hot high
mild high cool normal cool normal
F ALSE TRUE F ALSE F ALSE F ALSE TRUE
no no yes yes yes no
Regression
• Classification learning, but class is continuous (numeric prediction)
• Learning is supervised
• Why is this distinct from Classification?
• In Classification, we can exhaustively enumerate all possible labels for a given instance; a correct prediction entails mapping an instance to the label which is truly correct
• In Regression, infinitely many labels are possible, we cannot conceivably enumerate them; a “correct” prediction is when the numeric value is acceptably close to the true value
Example Predictions for weather
Outlook sunny sunny overcast rainy rainy rainy overcast sunny sunny rainy sunny overcast overcast rainy
Humidity Windy Play 85 FALSE no 90 TRUE no 86 FALSE yes 96 FALSE yes 80 FALSE yes 70 TRUE no 65 TRUE yes 95 FALSE no 70 FALSE yes 80 FALSE yes 70 TRUE yes 90 TRUE yes 75 FALSE yes 91 TRUE no
Actual Temp
Classified Temp
75 72 81 71
68.8 71.2 70.6 76.5
The MNIST digit classification data set
• Design a classification task given this data set. What ‘concept(s)’ could
be learnt?
• Could we perform clustering instead? What would change? • Can you think of a meaningful regression task?
Instance Topology
• Instances characterised as “feature vectors”, defined by a predetermined set of attributes
• Input to learning scheme: set of instances/dataset
• Flat file representation
• No relationships between objects
• No explicit relationship between attributes
• Attribute Data types
1. discrete: nominal (also: categorical) or ordinal
2. continuous: numeric
What about class label data types?
Nominal Quantities
• Values are distinct symbols (e.g. {sunny,overcast,rainy}) • values themselves serve only as labels or names
• Also called categorical, or discrete
• Special case: dichotomy (“Boolean” attribute)
• No relation is implied among nominal values (no ordering or distance measure), and only equality tests can be performed
Ordinal Quantities
• An explicit order is imposed on the values (e.g. {hot,mild,cool} where hot > mild > cool)
• No distance between values defined and addition and subtraction don’t make sense
• Example rule: temperature < hot →play = yes
• Distinction between nominal and ordinal not always clear (e.g. outlook)
Numeric Quantities
• Numeric quantities are real-valued attributes
• Scalar (a single number): attribute distance
• Vector-valued (a vector of numbers each pertaining to a feature or feature value): attribute position (x,y coordinate)
• All mathematical operations are allowed (of which addition, subtraction, scalar multiplication are most salient, but other operations are relevant in some contexts)
Attribute Types and Machine Learning Models
Most machine learning algorithms assume a certain type of attribute
• Naive Bayes: nominal or numeric
• Logistic/Linear Regression: numeric • Perceptron/Neural networks: numeric
If we have the wrong attribute type for our algorithm (or attributes with different types for each instance), we can
• Select only attributes with the correct type
• Change the model assumptions to match the data • Change the attributes to match the model
Converting Nominal to Numeric Attributes
Option 1: Map category names to numbers
• “red”, “blue”, “green”, “yellow”
Graphical representation:
• Problem: creates an artificial ordering. Some categories will appear more similar to each other than others
• Especially problematic with a large number of categories
Converting Nominal to Numeric Attributes
Option 2: One-hot encoding
“red” = “blue” = “green” = “yellow” =
[1, 0, 0, 0] [0, 1, 0, 0] [0, 0, 1, 0] [0, 0, 0, 1]
Graphical representation:
• Better way of encoding categorical attributes in a numeric way • Problem: increases the dimensionality of the feature space
Numeric Feature Normalization
Features of vastly different scale can be problematic
• Some machine learning models assume features to follow a normal distribution
• Some learning algorithms are overpowered by large feature values (and ignore smaller ones)
• Feature standardization rescales features to be distributed around a 0 mean with a unit standard deviation.
x′ = x − μ σ
• Feature scaling rescales features to a given range. For example, Min-max scaling rescales values between 0 and 1 using the minimum and maximum feature value observed in the data
x′= x−xmin xmax −xmin
Converting Numeric to Nominal Attributes
Distcretization: Group numeric values into a pre-defined set of distinct categories. E.g., map housing prices to {“high”, “medium”, “low”}
To do this, we
• First, decide on the number of categories
• Secondly, decide on the category boundaries
Converting Numeric to Nominal Attributes
Distcretization: Group numeric values into a pre-defined set of distinct categories. E.g., map housing prices to {“high”, “medium”, “low”}
To do this, we
• First, decide on the number of categories
• Secondly, decide on the category boundaries
Option1: Equalwidthsdiscretisation
• Find the minimum and maximum of the data
• Partition the values into n bins of width (max-min)/n bins
• Problem 1: outliers
• Problem 2: bins may end up with vastly different number of items • Problem 3: how to select n?
Converting Numeric to Nominal Attributes
Distcretization: Group numeric values into a pre-defined set of distinct categories. E.g., map housing prices to {“high”, “medium”, “low”}
To do this, we
• First, decide on the number of categories
• Secondly, decide on the category boundaries
Option2: Equalfrequencydiscretisation
• Sort the values
• Partition them into n bins such that each bin has an identical number of items
• Problem 1: boundaries could be hard to interpret
• Problem 2: how to select n?
Converting Numeric to Nominal Attributes
Distcretization: Group numeric values into a pre-defined set of distinct categories. E.g., map housing prices to {“high”, “medium”, “low”}
To do this, we
• First, decide on the number of categories
• Secondly, decide on the category boundaries
Option3: Clustering
• Use unsupervised machine learning to group the value into n clusters • For example: K-means clustering (more on that later)
• Problem 1: how to evaluate the result?
• Problem 2: how to select K ?
ML in the Wild
Preparing the Input
• Problem: different data sources (e.g. sales department, customer billing department, ...)
• Differences: styles of record keeping, conventions, time periods, data aggregation, primary keys, errors
• Data must be assembled, integrated, cleaned up • Data warehouse: consistent point of access
• External data/storage may be required
• Critical: type and level of data aggregation
Missing Values
• The number of attributes may vary in practice • missing values
• inter-dependent attributes
• Typical cases of out-of-range values:
• Types: unknown, unrecorded, irrelevant • Reasons:
• malfunctioningequipment
• changesinexperimentaldesign • collationofdifferentdatasets
• measurementnotpossible
• Most models assume that values are missing at random
• Missing value may have significance in itself (e.g. missing test in a medical examination) → Missing not at random.
Missing may need to be coded discretely.
Inaccurate Values
• Cause: a given data mining application is often not known at the time logging is set up
• Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer)
• Typographical errors in nominal attributes →values need to be checked for consistency
• Typographical and measurement errors in numeric attributes →outliers need to be identified
• Errors may be deliberate (e.g. wrong post codes)
Getting to Know the Data
• Simple visualization tools are very useful
• Nominal attributes: histograms (distribution consistent with
background knowledge?)
• Numeric attributes: scatter plots (any obvious outliers?)
• 2-D and 3-D plots show dependencies
• Need to consult domain experts
• Too much data to inspect? Take a sample!
• You can never know your data too well (or can you?)
Coding Demo!
Intended take-aways
• Starting Jupyter Notebook
• Reading in a dataset (using basic Python)
• Reading in a dataset (using the pandas library)
• Formatting a dataset into lists (of instances)
• Separating features from class labels (for each instance)
Today: establishing common vocabulary
• What are instances, attributes and concepts?
• Learning paradigms: supervised and unsupervised • Concepts: Regression, Classification, Clustering
• Attributes: types and encodings
• Python and Jupyter
Next: K-Nearest Neighbors
