The best complete summary for Data Science Methods for MADS (EBM215A05), it includes: All Lectures and the latest Practice Exam. Enhanced with a dynamic table of contents and meticulous organization for readability and easy studying. 100% of profit from this summary is donated to local Groningen NG...
SUMMARY OF EVERYTHING YOU NEED
ALL LECTURES + EXAM 2021-22
E n h a n c e d w i t h a d y n a m ic t a b le o f c o n t e n t s .
, MADS MADLAD |2
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Table of Contents
Week 1............................................................................................................... 8
Lecture 1: Introduction to Machine Learning .................................................. 8
Data Science Process ................................................................................... 8
Criteria for a good model ............................................................................. 9
What is (Machine) Learning? ....................................................................... 9
3 Types of ML Models ................................................................................ 10
ML Techniques........................................................................................... 11
Why ML?.................................................................................................... 12
Statistics vs. ML vs. AI ................................................................................ 12
ML Modelling Process (3 Steps) ................................................................. 12
ML Model Process – In Practice: Learning to filter spam............................ 14
Assessing the ML Process .......................................................................... 16
Overfitting & Underfitting.......................................................................... 17
Measures for assessing model quality ....................................................... 18
Data (pre-)processing ................................................................................ 18
Goal of Data Exploration ............................................................................ 18
Steps in Data Exploration ........................................................................... 19
Logistic Regression..................................................................................... 19
Estimation – Beta’s (β) ............................................................................... 21
Interpretation ............................................................................................ 21
In Practice – Titanic Data ........................................................................... 22
Deciding on IVs .......................................................................................... 23
Model Validation (1) – Making Predictions (in R) ....................................... 23
Model Validation (2) – 3 Forms of Validation Criteria ................................ 23
Hit Rate (1) – Interpretation & Calculation ................................................ 23
Hit Rate (2) – How to in R........................................................................... 24
Top Decile Lift (1) – Interpretation & Calculation ....................................... 24
Top Decile Lift (2) – How to in R ................................................................. 25
Top Decile Lift (3) - Lift Curve: Interpretation ............................................ 25
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Top Decile Lift (3) - Lift Curve: How to in R................................................. 25
GINI Coefficient (1) – Interpretation & Calculation .................................... 26
GINI Coefficient (2) – How to in R .............................................................. 26
Fit Criteria (1) – Calculation ....................................................................... 26
Fit Criteria (2) – Calculation: Solving overfitting ......................................... 26
Fit Criteria (3) – How to in R ....................................................................... 27
Balanced vs. Unbalanced Sample............................................................... 27
Week 2............................................................................................................. 28
Lecture 2: Stepwise LR, Tree models, Bagging, and Boosting ........................ 28
Overview: Boosting & Bagging techniques................................................. 28
Stepwise Logistic Regression (SLR) ............................................................. 28
3 Types of Stepwise Regressions................................................................ 29
SLR – How to in R ....................................................................................... 29
Tree Models – Decision Trees .................................................................... 30
How to grow a tree: Splitting logic & rules................................................. 31
Splitting Rule for CHAID ............................................................................. 31
Splitting Rule for CART ............................................................................... 32
Splitting rule for C4.5 ................................................................................. 34
Which splitting rule is the best? ................................................................. 34
Regression-type Problem: CART or CHAID? ............................................... 35
Comparing Predictive Ability of Models ..................................................... 36
Finding the right Tree Size ......................................................................... 37
Pruning: Cost Complexity Pruning.............................................................. 37
Comparing Trees (example) ....................................................................... 38
Trees: Useful as a variable selection tool ................................................... 39
Disadvantages of tree models.................................................................... 40
CART – How to in R .................................................................................... 40
CHAID – How to in R .................................................................................. 42
Entropy (C5.0) – How to in R ...................................................................... 42
Ensemble Learning..................................................................................... 42
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Popular Ensemble Methods – Bagging, Boosting & Random Forest........... 43
Bagging: Bootstrap AGGregatING .............................................................. 43
Boosting..................................................................................................... 44
Bagging vs. Boosting .................................................................................. 46
Boosting – How to in R............................................................................... 46
Bagging – How to in R ................................................................................ 47
Pros & Cons: Log-regression vs. Trees vs. Bagging/Boosting ...................... 47
Week 3............................................................................................................. 48
Lecture 3: Random forests, Support Vector Machines, & Artificial Neural
Networks ...................................................................................................... 48
Random Forest .......................................................................................... 48
Support Vector Machines (SVM) ................................................................ 50
o Gaussian Radial Basis Function (RBF) ................................................... 54
Artificial Neural Networks .......................................................................... 54
Week 4............................................................................................................. 59
Lecture 4: Regularization .............................................................................. 59
Regularization ............................................................................................ 59
Linear Regression – Least Squares Regression (OLS) .................................. 59
Regularization technique 1: Forward Stepwise Selection ........................... 63
Regularization Technique 2: Ridge regression............................................ 67
Regularization Technique 3: Lasso regression ............................................ 68
Regularization Technique 4: Elastic-net regression .................................... 68
How to in R – Ridge, Lasso and Elastic-net regression ................................ 69
Hints on Assignment 2: .............................................................................. 73
Week 5............................................................................................................. 74
Lecture 5: Multi-armed Bandits .................................................................... 74
What is a multi-armed bandit problem? .................................................... 74
Epsilon Greedy Algorithms......................................................................... 77
Upper Confidence Bound algorithms (UCB) ............................................... 81
Thompson sampling algorithm .................................................................. 83
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Bandits with Expert Advice ........................................................................ 85
Week 6............................................................................................................. 88
Lecture 6: Trustworthy AI ............................................................................. 88
What is trustworthy AI? ............................................................................. 88
Morality ..................................................................................................... 89
Incorporating Ethics into Marketing Decisions ........................................... 91
3 Stages in the ML flow prone to bias ........................................................ 92
Privacy ....................................................................................................... 93
2 Important laws in EU and USA on Privacy (GDPR & CCPA) ...................... 94
Week 7............................................................................................................. 97
Lecture 7: Causality and other ML issues ...................................................... 97
Churn probability vs. Change in churn ....................................................... 97
Limitations of correlation-based techniques .............................................. 97
Causality or Correlation (Criteria) .............................................................. 98
Uplift modeling .......................................................................................... 99
Extensions on non-binary outcomes ........................................................ 102
Predictive validity measures (PVM) ......................................................... 103
Example Exam 2021-22 .................................................................................. 106
, MADS MADLAD |8
Week 1
Lecture 1: Introduction to Machine Learning
Data Science Process
- Defining business problem (1)
o Ask questions to discover the real problem
▪ Management dilemma, questions
▪ Research questions
- Design the Research (2)
o Formulate hypotheses
o Literature research
o Define Constructs
- Data Collection & Preparation (3)
o Extracting data from sources
o Data Cleaning
o Data transformation (e.g., new variables)
- Explorative Analysis (4)
o Correlations, statistical tests, histograms, etc.
- Modelling (5)
o Specification (e.g., type and structure)
o Estimation
o Validation
o Interpretation
- Implementation (6)
o Communicating the results to stakeholders
o Data-driven storyline & visualization
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- Monitoring (7)
o Monitoring the model’s performance
Criteria for a good model
- Simple
- Evolutionary
o Starting simple but building it up
- Complete
o As complete and simple as possible
- Adaptive
- Robust
o Able to use it in different circumstances
(e.g. during inflation, COVID, etc.)
What is (Machine) Learning?
Machine learning is concerned with computer programs that automatically
improve their performance through experience.
- Branch of AI and CS, which focuses on use of data and algorithms to
imitate the way that humans learn.
, M A D S M A D L A D | 10
3 Types of ML Models
- Supervised: uses a training set, including both input and correct (e.g.
labeled) output, to teach models to yield the desired output.
o Input + Annotations -> Model -> Prediction
o Used for: classificaition (sorting items into categories), regressions
- Unsupervised: Identifies patterns in data sets containing data points
that are neither classified nor labeled.
- Reinforcement: enforces models (gives feedback or corrections) to learn
how to make predictions.
- Visual Examples of the 3 Types:
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