Concept map: Selection and survivorship bias

Prerequisites (two levels) and direct dependents of Selection and survivorship bias. Show everything

3prerequisites shown
1direct dependents
2lessons use it

Taught and used in

day 5 day 8

Causality and experimentation, Statistics and probability

Insights 32

Treat unlabeled cases as unknown, not negative, when positives are only partly recorded
Treat clicks on a ranked list as position-biased before training or evaluating a ranker on them
Weight validation folds by the input density ratio when the scoring population differs in its inputs
Bracket a suspected MNAR column with a delta adjustment instead of arguing about MAR versus MNAR
Build the golden set from stratified production traffic, not from questions developers imagine
Read thumbs-up and thumbs-down rates as a selection-biased signal and sample the silent majority
Blind and shuffle annotation tasks, and seed known-answer items to catch drift
Exclude immature labels when building training data for slow outcomes

Projects 11

List view151 concepts

Day 1
- Baseline-first culture · needs When rules beat ML
- Business vs analytical problems
- Cost of being wrong · needs Business vs analytical problems
- Prediction · needs Business vs analytical problems
- When rules beat ML · needs Business vs analytical problems
Day 2
- Data contracts · needs Unit of analysis and grain
- Horizon · needs Target definition
- Leakage · needs Point-in-time correctness
- Point-in-time correctness · needs Horizon
- Target definition · needs Unit of analysis and grain
- Unit of analysis and grain · needs Business vs analytical problems
Day 3
- Common distributions · needs Random variables and expectation
- Conditional probability and Bayes · needs Random variables and expectation
- Random variables and expectation
- Sampling distributions · needs Variance and covariance, Common distributions
- Variance and covariance · needs Random variables and expectation
Day 4
- Bayesian and frequentist thinking · needs Confidence intervals, Conditional probability and Bayes
- Confidence intervals · needs Sampling distributions
- Effect size and power · needs Hypothesis tests and p-values
- Hypothesis tests and p-values · needs Sampling distributions
- Multiple comparisons · needs Hypothesis tests and p-values
Day 5
- Confounding · needs Prediction, Variance and covariance
- DAGs · needs Confounding
- Forecasting vs causal what-if · needs Confounding
- Quasi-experimental designs · needs DAGs
- Randomized experiments · needs Hypothesis tests and p-values, Confounding
- Selection and survivorship bias · needs Confounding
- Simpson's paradox · needs Confounding
Day 6
- Duplicates and integrity rules · needs Unit of analysis and grain
- Hypothesis-driven EDA · needs Unit of analysis and grain, Common distributions
- Missing data mechanisms · needs Hypothesis-driven EDA
- Outliers · needs Hypothesis-driven EDA
- Temporal gaps and irregular timestamps · needs Hypothesis-driven EDA
Day 7
- Event vs snapshot data and event vs processing time · needs Unit of analysis and grain, Point-in-time correctness
- Joins and join explosions · needs Unit of analysis and grain
- Slowly changing dimensions and backfills · needs Event vs snapshot data and event vs processing time
- Window functions · needs Joins and join explosions
Day 8
- Chart choice · needs Hypothesis-driven EDA
- Driver and contribution analysis · needs Chart choice
- Insight-led titles and storytelling · needs Chart choice
- Misleading charts and interpretation limits · needs Chart choice, Selection and survivorship bias
Day 9
- Categorical encoding · needs Holdout boundary before fitting
- Fold-safe target encoding · needs Categorical encoding
- Holdout boundary before fitting · needs Leakage
- Preprocessing pipelines · needs Scaling and when it is unnecessary, Categorical encoding
- Scaling and when it is unnecessary · needs Holdout boundary before fitting
Day 11
- Linear regression · needs Variance and covariance, Baseline-first culture
- Logistic regression and log loss · needs Linear regression, Conditional probability and Bayes
- Multicollinearity · needs Linear regression
- Regression assumptions and residuals · needs Linear regression
- Regularization · needs Linear regression, Scaling and when it is unnecessary
Day 12
- Bagging and random forests · needs Decision trees
- Decision trees · needs Baseline-first culture
- Gradient boosting · needs Decision trees
- XGBoost · needs Gradient boosting
Day 13
- Class imbalance · needs Logistic regression and log loss
- Probability calibration · needs Logistic regression and log loss
- Resampling · needs Class imbalance, Holdout boundary before fitting
- Thresholds from cost matrices · needs Class imbalance, Cost of being wrong
Day 14
- Anomaly detection · needs Outliers, Clustering
- Cluster validation and actionability · needs Clustering
- Clustering · needs Scaling and when it is unnecessary, Variance and covariance
- PCA and dimensionality reduction · needs Variance and covariance, Scaling and when it is unnecessary
Day 15
- Cross-validation schemes · needs Train / validation / test
- Leakage taxonomy · needs Leakage, Cross-validation schemes
- Learning curves and early stopping · needs Train / validation / test
- Nested CV · needs Cross-validation schemes, Multiple comparisons
- Train / validation / test · needs Holdout boundary before fitting
Day 16
- Business vs ML metric · needs Regression metrics, Cost of being wrong
- Classification metrics · needs Thresholds from cost matrices
- Forecast metrics · needs Regression metrics
- Regression metrics · needs Random variables and expectation
Day 17
- Feature importance · needs Bagging and random forests, Multicollinearity
- Feature selection inside CV · needs Feature importance, Cross-validation schemes
- PDP · needs Feature importance
- SHAP · needs Feature importance
Day 18
- Error slices and residual patterns · needs Regression assumptions and residuals, Classification metrics
- Root-cause analysis and failure taxonomies · needs Error slices and residual patterns
Day 19
- Batch vs online serving and deployment · needs Reproducibility and versioning
- Drift and monitoring · needs Batch vs online serving and deployment, Common distributions
- Experiment tracking and model registry · needs Reproducibility and versioning
- Reproducibility and versioning · needs Preprocessing pipelines
- Training-serving skew · needs Batch vs online serving and deployment, Preprocessing pipelines
Day 21
- Forecasting workflow · needs Trend, Target definition
- Stationarity · needs Trend, Variance and covariance
- Structural breaks and regimes · needs Trend
- Time series data types · needs Trend
- Trend · needs Hypothesis-driven EDA, Temporal gaps and irregular timestamps
Day 22
- Autocorrelation · needs Variance and covariance, Stationarity
- Differencing · needs Stationarity
- STL and decomposition · needs Trend
- White noise and Ljung-Box · needs Autocorrelation, Hypothesis tests and p-values
Day 23
- ARIMA and SARIMA · needs Autocorrelation, Differencing
- Exponential smoothing and ETS · needs Forecast baselines
- Forecast baselines · needs Forecasting workflow, Baseline-first culture
Day 24
- Direct · needs Point-in-time lag and rolling features
- Exogenous variables and availability · needs Point-in-time lag and rolling features
- Global ML forecasting models · needs Gradient boosting, Point-in-time lag and rolling features
- Point-in-time lag and rolling features · needs Window functions, Autocorrelation, Point-in-time correctness
Day 25
- Per-horizon error and stability · needs Rolling-origin backtesting
- Rolling-origin backtesting · needs Cross-validation schemes, Forecast baselines
Day 26
- Conformal prediction · needs Coverage, Train / validation / test
- Coverage · needs Prediction intervals and quantiles
- Forecast combinations · needs Forecast baselines
- Forecast decision economics · needs Prediction intervals and quantiles, Cost of being wrong
- Hierarchical forecasting and reconciliation · needs Time series data types, Forecast baselines
- Intermittent demand · needs Time series data types, Forecast metrics
- Prediction intervals and quantiles · needs Confidence intervals, Forecast baselines
Day 27
- Deep and foundation forecasting models · needs Global ML forecasting models, Rolling-origin backtesting
Day 28
- Capacity-censored demand · needs Target definition, Forecasting workflow
Day 29
- Clarifying ambiguous cases · needs Business vs analytical problems
Day 31
- Neurons · needs Linear regression
- Tensors · needs Neurons
Day 32
- Activation functions · needs Neurons
- Losses and output layers · needs Activation functions, Logistic regression and log loss
Day 33
- Backpropagation · needs Losses and output layers, Tensors
- Optimizers and learning rates · needs Backpropagation
Day 34
- Training dynamics and regularization · needs Optimizers and learning rates, Learning curves and early stopping
Day 35
- Neural vs classical baseline comparison · needs Training dynamics and regularization, Baseline-first culture
Day 36
- Convolutional networks · needs Tensors
Day 37
- Recurrent networks · needs Backpropagation
Day 38
- Embeddings and representation learning · needs Neurons, Variance and covariance
Day 39
- Attention and Transformers · needs Embeddings and representation learning, Activation functions
Day 40
- Transfer learning and fine-tuning decisions · needs Training dynamics and regularization
Day 41
- Tokenization · needs Categorical encoding
Day 42
- Decoding and sampling · needs Next-token prediction and context windows
- Hallucination and unsupported output · needs Next-token prediction and context windows
- Next-token prediction and context windows · needs Attention and Transformers, Tokenization
Day 43
- Prompting vs retrieval vs fine-tuning · needs Next-token prediction and context windows
- Versioned evaluation set before adaptation · needs Train / validation / test, Prompting vs retrieval vs fine-tuning
Day 44
- PEFT and LoRA · needs Transfer learning and fine-tuning decisions, Prompting vs retrieval vs fine-tuning
Day 45
- LLM evaluation · needs Versioned evaluation set before adaptation
- RLHF and DPO fundamentals · needs LLM evaluation
Day 46
- Generative model families · needs Embeddings and representation learning
Day 47
- Structured outputs and validation · needs Next-token prediction and context windows
- Untrusted input boundaries · needs Structured outputs and validation
Day 48
- Lexical · needs RAG pipeline
- RAG pipeline · needs Embeddings and representation learning, Tokenization, Untrusted input boundaries
Day 49
- Agents and tool use · needs Structured outputs and validation, Untrusted input boundaries
- Agents vs deterministic workflows · needs Agents and tool use
Day 51
- Faithfulness and groundedness · needs Retrieval metrics, Hallucination and unsupported output
- Retrieval metrics · needs RAG pipeline
Day 52
- LLM request lifecycle and observability · needs Batch vs online serving and deployment, Next-token prediction and context windows
Day 53
- Privacy · needs Data contracts
- Prompt injection and AI security · needs Untrusted input boundaries, Agents and tool use
Day 54
- AI cost · needs LLM request lifecycle and observability
Day 55
- Production GenAI design · needs RAG pipeline, Prompt injection and AI security, AI cost
Day 58
- Decision and assumption logs · needs Batch vs online serving and deployment
- ML system design · needs Drift and monitoring, Decision and assumption logs
Day 59
- Interview defense and revision · needs Clarifying ambiguous cases
Day 60
- Portfolio evidence · needs Interview defense and revision

Taught and used in

Insights 32

Projects 11

List view151 concepts

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