Chapter 4 microsoft azure machine learning studio

Chapter 4
Microsoft Azure
Machine Learning Studio
The Presentation Slides for Teaching
Financial Regulations and Regulatory Technology
Website : https://sites.google.com/site/quanrisk
E-mail : quanrisk@gmail.com
Copyright © 2021 Dr. LAM Yat-fai

Declaration
 Copyright © 2021 Dr. LAM Yat-fai
 All rights reserved. No part of this presentation file may be
reproduced, in any form or by any means, without written
permission from Dr. LAM Yat-fai.
 Authored by Dr. LAM Yat-fai (林日辉),
Chief Data Scientist, CapitaLogic Limited,
Adjunct Professor of Finance, City University of Hong Kong,
Doctor of Business Administration,
CFA, CAIA, CAMS, CFE, FRM, PRM, MCSE, MCNE.
Copyright © 2021 Dr. LAM Yat-fai 2

Outline
 Monotonic causal relationship
 Sample data set
 Feature selection
 Two class model
 Prediction model
 Regression model

Monotonic causal relationship
 Label
 Response variable
 Features
 Explanatory variables
 Noise
 Unexplainable effect
 
1 2 3 N
x , x , x ,
y = F + N
… ,x oise

Label
 y
 The value largely determined by the features
 To be predicted today
 Two class: Up, down
 Multiple class: A, B, C, D
 Any value: from -∞ to ∞
 To be observable some times later after the
prediction

Features
 x1, x2, x3, … xN
 Numeric
 Largely determine the value of the label
 Observable
 Measureable
 Monotonically related to the label

Noise
 Unobservable and/or immeasurable
 Small noise
 Most critical features are ready
 Can explain the majority of the label
 Large noise
 Some critical features are missed
 Fail to explain the majority of the label

A monotonic relationship
x1 x2 y
+
↑
↑
- ↑
1 2
y = x + x

Not a monotonic relationship
x1 x2 y
+
↑
↑
- ↓
1 2
y = x × x

A monotonic relationship
x2 x2 y
+
↑
↑
- ↓
2 2
1 2
y = x + x

Example
 Label
 The chance that a student can graduate successfully
from a university master programme
 Features
 Undergraduate results ↑
 Financial resources ↑
 Disability ↓
 Noise
 Pressure
 Sickness
 Luck

Machine learning
 Historical records
 A set of data recording the label and features in the
past
 A hypothetical assumption
 To be discovered by machine learning algorithms
 Prediction
 To estimate the label before it becomes observable

Machine learning framework
Copyright © 2020 CapitaLogic Limited 13
Machine
learning
Historical
records
Monotonic
causal
relationship
Prediction

Machine learning approaches
Traditional
Rapid
development
Web
servcie
Development Personal computer Personal computer Web
Execution Personal computer
Personal computer
/ Web
Web
Programming Yes No No
Platform
Python, R,
Scikit-learn
RapidMiner,
Weka, Orange
Google, Amazon,
Microsoft, IBM
Target user Programmer Data scientist
Risk manager and
compliance officer

Microsoft Machine Learning
Azure ML
 Paid service
 Integrating with all
Azure products
 Highly technical
 For real life application
Azure ML Studio (Classic)
 Free service
 Standalone
 Easy to use
 Easy to make mistake
 For proof of concept

Outline
 Sample data set
 Two class model

Full data set
 Data
 A lot of historical records with correct values
 Good records
 Label
 Major features monotonically impacting the label
 Bad records
 With extreme values of label and/or features
 With missed label and/or features
 Duplicated records

Record preparation
 Outliers
 Largest or smallest 1% values of a feature/label
 To be replaced with missing value “”
 Missing values
 To delete records with missing value
 Duplicated records
 To delete duplicated records
 Random sample
 Stratified sampling

Sign in

Datasets

From Full data set
To Sample data set (1)

From Full data set
To Sample data set (2)

Prepare sample data set (1)
 Dataset
 Chapter 4a1 – Full data set.csv
 Clip Values
 Threshold Percentile
 Substitute value Missing
 List of columns x1,x2,x3,x4,x5,x6
 Clean Missing Data
 Columns to be cleaned y,x1,x2,x3,x4,x5,x6
 Cleaning mode Remove entire row

 Remove Duplicate Rows
 Key column selection y,x1,x2,x3,x4,x5,x6
 Split Data
 Fraction of rows 1
 Split Data
 Splitting mode Regular Expression
 Regular Expression ”y”^0

 Partition and Sample
 Number of rows 400
 Add Rows
 Chapter 4a2 – Sample data set
 Convert to CSV

Sample size
 400 records in each category [0, 1]
 200 records for training data set
 To build the model
 100 records for validation data set
 To calibrate the best set of model parameters
 100 records for testing data set
 To assess the accuracy of the model
Example 4.a.2

Outline
 Sample data set
 Two class model

Monotonicity
 Between the label and a feature
 Quantified by the p-value
 A smaller p-value suggests a stronger
monotonicity
 To exclude weak monotonic features
Example 4.a.3

p-value
 2-mean t-test
 p-value
 < 5% suggests good monotonicity in general
   
 
 
2 2
0 1
0 1
0 1
2
2 2
0 1
0 1
4 4
0 1
2 2
0 0 1 1
SD SD
Standard error = +
N N
x - x
t-statistic =
Standard error
SD SD
+
N N
df =
SD SD
+
N N -1 N N -1
p-value = TDIST ABS t-statistic ,df,2
 
 
 

Principal components
 Principal components
 Linearly transformed independent features
 How many principal components are
sufficient?
 Sum of eigenvalues > 95% is good in general
Example 4.a.4

Features and principal components
 Label
 0, 1
 Features
 x1, x2, x4, x5, x6
 Principal components
 4

Outline
 Sample data set
 Two class model

From Sample data set
To Prediction model (1)

Prediction model (1)
 Dataset
 Chapter 4a3 – Sample data set
 Select Columns in Dataset
 Select columns y,x1,x2,x4,x5,x6
 Normalize Data
 Columns to transform x1,x2,x4,x5,x6

 Principal Component Analysis
 Selected columns x1,x2,x4,x5,x6
 Number of dimensions 4
 Normalize dense columns Blank
 Split Data × 2
 Stratified split True
 Stratification key column y
 Two-Class Neural Network

 Tune Model Hyperparameters
 Label columns y
 Score Model
 Evaluate Model

Normalization
 To transform all features into a compatible
range
 
   
 
k k
k
k
x i - Average All x
z i =
S.D. All x

Score Model

Two group classification
41
Copyright © 2020 CapitaLogic Limited
Middle cutoff

Evaluate Model

Model performance
Model prediction
1 0
Testing
data
1
True
positive
False
negative
0
False
positive
True
negative

Three group classification
44
Lower cutoff
Upper cutoff

Cutoff scores
 Score
 Probability of Label = 1
 Unbiased cutoff score
 50%
 Upper cutoff score
 The minimum score above which false positive is < 1%
 Lower cutoff score
 The maximum score below which false negative is < 1%
 Data quality
 Good: Large positive and negative zones, small noise
 Bad: Small middle zone, large noise

Four group classification
 Upper group
 Above upper cutoff score
 Upper-middle group
 Between upper and middle cutoff scores
 Lower-middle group
 Between middle and lower cutoff scores
 Lower group
 Below cutoff score

Four group classification
47
Lower cutoff
Upper cutoff
Middle cutoff

Outline
 Sample data set
 Two class model

Predictive Web Service

Select Inputs

Select Outputs

Deploy Web Service

Download Excel prediction model

Prediction with Excel for web
 Register the Microsoft OneDrive with a personal
e-mail account (gmail, hotmail, qq)
 Upload the Excel prediction model to OneDrive
 Click on the prediction model to open the Excel
prediction model
 Warning
 Never register the Microsoft OneDrive using your
company or school e-mail address
 Never use the Microsoft Excel desktop edition to
conduct prediction

Microsoft OneDrive

Prediction

Outline
 Sample data set
 Two class model

Datasets

 Dataset
 Chapter 4b1 – Full data set.csv
 Clip Values
 Threshold Percentile
 Substitute value Missing
 List of columns y,x1,x2,x3,x4,x5,x6
 Clean Missing Data
 Columns to be cleaned y,x1,x2,x3,x4,x5,x6
 Cleaning mode Remove entire row

 Remove Duplicate Rows
 Key column selection y,x1,x2,x3,x4,x5,x6
 Split Data
 Fraction of rows 1
 Partition and Sample
 Number of rows 400
 Convert to CSV

p-value
 Correlation t-test
 p-value
 < 5% suggests a good monotonicity in general
 
 
2
1 - ρ
Standard error =
N - 2
ρ
t-statistic =
Standard error
df = N - 2
p-value = TDIST ABS t-statistic ,df,2

Two correlation tests
 Pearson correlation coefficient
 Rank correlation coefficient

Sample size
 400 records
 200 records for training data set
 To build the model
 100 records for validation data set
 To calibrate the best set of model parameters
 100 records for testing data set
 To assess the accuracy of the model
Example 4.c.2

 Dataset
 Chapter 4b3 – Sample data set
 Select Columns in Dataset
 Select columns y, x1, x2, x4, x5, x6
 Normalize Data
 Columns to transform y, x1, x2, x4, x5, x6

 Principal Component Analysis
 Selected columns x1,x2,x4,x5,x6
 Number of dimensions 4
 Normalize dense columns Blank
 Split Data
 Stratified split False
 Linear Regression

 Tune Model Hyperparameters
 Label columns y
 Score Model
 Evaluate Model

Score Model

Evaluate Model

What is a liner regression?
A best fit straight line only

Common issues
 Excel prediction model does not work
 Use the Excel web edition
 Download a new prediction model Excel file

Chapter 4 microsoft azure machine learning studio

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