Machine Learning

A walk through Vienna in the name of Machine Learning.

Us General Concepts

• Bed Stands: Type 1 and Type 2 error:
• Seb: H0: Someone who has Covid tests positive.
• Barry: Recall/Type 1 error: Someone who does not have Covid tests positive (bad for the individual).
  • True Positive / True Positive + False Negative
• Blue: Precision/Type 2 error: Someone who has Covid tests negative (bad for everyone).
  • True Positive / True Positive + False Positive

My Room Theories

• Chair: Behavioral Change: operant (response is affected by consequences, e.g., gamification) vs classical conditioning (response occurs to stimuli, e.g., reminders)
• Box: Personal Thoughts: Precontemplation > Contemplation > Preparation > Action > Maintenance (actively set goals for users and encourage them to hit the goals).
• Blanket: Social Imitation: Consider collaboration features.
• Window: Fogg Model of Change: pleasure / pain, hope / fair and social acceptance get lazy people to do things.
• Shutters: ABA Model of Change: people engage in behavior to (a) achieve a desired result or (b) avoid an undesired result
• Radiator: Randomized Variable Schedule: slot machines are addictive because the randomize administering awards
• Laundry: Outsized positive rewards & mitigating losses: loss aversion
• Robe: Path dependence for least resistance: one step leads to the next step

Bath room ML Terminology

• Bath Ward Robe: Supervised Learning: develop predictive models based on input and output (classification and regression).
  • Contacts: K-Nearest Neighbors: birds of a feather will flock together.
  • Gel: Linear regression: identify lines that best fits data points.
  • Lip stick: Logistic regression: used for binary outcome, probs of accepting a friend request.
  • Deo: Decision trees: best of 20 questions.
  • Perfume: SVMs: perceptron machine divides two dimensional space.
• Towels: Unsupervised Learning: group and interpret data based on input data.
  • Small: K-means: bucket observations in k clusters.
  • Large: PCA: converts correlated variables into linearly uncorrelated vectors.
• Spanners: Dependent & independent variables: labels & key variable of interest.
• Pillow Cases: Confounding variables: variables that could drive spurious correlations.
• Blankets: Different Test Statistics
  • Paper rolls: z-test / z-score: two populations, normal distribution
  • Tissues: t-test / t-statistic: two small samples, normal distribution
  • Window cleaner: Anova / f-statistic: three or more samples, normal distribution
  • Toilet cleaner: chi-squared test / chi-squared: two samples, any distribution
• Sink: Regression terms:
  • Silvio Brush: Lasso (L1) Regularization: takes absolute value to avoid overfitting
  • My brush: Ridge (L2) Regularization: takes squared value to avoid overfitting
  • Toothpaste: Normalization: adjusting different scales to same scale
  • Lights: One-hot encoding: when we have data on more than one level (fe/male)
  • Mirror: Underfitting: too simple to explain variance
  • Shower: Overfitting: force-fitting, too good to be true

Silvio's office Validation of Supervised Learning:

• Massive picture: Train, test validation: Test different models on test data / val data.
• Coffee Machine: K-fold cross validation: Create k-folds and use these as test data.
• 3D Printer: F1-score: 2 * 1/recall + 1/precision = (sensitivity = recall, specificity = 1-precision).
• 3D Printer Workshop: Area under ROC: visualization of classification error
• Desk: Ad-Hoc Experiments - A/B Testing:
  • Chair: Approach: experiment run on two groups of random users (RCT), one retrieving the treatment and one that does not.
  • Srreen: Premise: all conditions have to be the same for treated and control groups, except for the treatment.
  • Silvio: ATE (average treatment effect): what is the average difference in our chosen metric between treated and control groups.
  • iPad: Example: Sending out friend requests with and without a personalized message.

Hover Post-Hoc Experiments - Causal Inference Techniques

• Window: Difference-in-Difference Modeling: find comparable counterfactual or control group to compare our treated users. (How do markets behave after airing or not airing a commercial in an area?)
• Wine: Placebo Testing: Test if there is no difference in two areas where there is no commercial aired.
• Book: Regression Discontinuity: Find comparable counterfactuals or control group members to compare treated users against.
• Towel box: Interrupted Time Series:
  • Whiteboard: Autocorrelation: serial correlation between values of the process at different times.
  • Wine shelve: Stationarity: Mean and variance are constant over time.
  • Gin & Tonic: ARMA & ARIMA: autoregression models are linear regression models where we predict series values on the lagged value of the series itself
  • Winter Wine w/ rum: Seasonality Decomposition: known patterns that are repeated over fixed time intervals.
• Light: Statistical Matching: achieve similar distributions on observed variables for treatment and control.
• Tado: Propensity Matching: classification algorithm to assign a treatment score or a probability of being treated.
• Table: General Heuristics: Educated guessing, dosage effect measuring, placebo tests, coherence, analogy.
• Salt: Uplift Modeling: targeting users that will modify their behavior based on treatment
• Pepper: Random Forests: collection of many decision trees to identify features and make predictions