Machine Learning Using Stata Training Course

Participants can prepare an analysis-ready dataset while preserving source-data traceability.

• Duplicate detection using isid, duplicates, and key-variable checks
• Missing-data patterns assessed with misstable summarize and tabulation
• Outlier investigation using percentiles, box plots, and domain thresholds
• Date and time variables converted into consistent Stata formats
• Merging panel, transaction, and reference files using documented identifiers
• Training dataset construction with explicit target, predictors, and observation windows

Participants can define a prediction problem and select an appropriate validation design.

• Continuous, binary, count, and time-to-event prediction targets
• Prediction objectives distinguished from causal estimation and policy evaluation
• Target leakage identified through timing, availability, and outcome contamination
• Training, validation, and test samples assigned using reproducible seeds
• Temporal splits designed for forecasting and changing economic conditions
• Unit-of-analysis choices for customers, loans, firms, and transactions

Participants can use exploratory analysis to identify usable predictors and data risks.

• Distribution checks using summarize, detail, histogram, and kdensity
• Categorical predictors inspected with tabulate, proportions, and sparse-cell counts
• Pairwise associations assessed with correlations, cross-tabs, and scatterplots
• Outcome prevalence examined before binary classification model selection
• Panel structure diagnosed with xtset, gaps, and repeated observations
• Economic plausibility checks applied to predictors, targets, and timestamps

Participants can create documented features suitable for regularised prediction models.

• Factor-variable notation creating categorical effects and interaction terms
• Logarithmic, ratio, growth, and difference transformations for skewed variables
• Lagged and rolling features constructed from panel and time-series data
• Binning continuous predictors using policy thresholds and quantile cut-points
• One-hot encoding choices assessed against reference-category specifications
• Feature dictionaries recording definitions, timing, source fields, and rationale

Participants can establish transparent benchmark models before applying machine learning methods.

• Ordinary least squares benchmarks estimated with regress and factor variables
• Binary outcome benchmarks estimated using logit and logistic commands
• Count outcome benchmarks estimated with poisson and negative binomial models
• Naive forecasting benchmarks based on persistence, averages, and seasonal patterns
• In-sample fit separated from out-of-sample predictive performance
• Benchmark documentation recording sample definitions, variables, and assumptions

Participants can explain and estimate lasso models for high-dimensional predictor sets.

• Overfitting, variance, bias, and shrinkage in predictive modelling
• L1 penalty mechanics and coefficient selection under lasso estimation
• L2 penalty mechanics and coefficient shrinkage under ridge regression
• Penalty parameter lambda selected through cross-validation procedures
• Standardisation effects considered before comparing penalised coefficient estimates
• Sparse models interpreted without treating selection as causal proof

Participants can estimate, tune and interpret lasso linear prediction models in Stata.

• Lasso linear fitted with lasso linear for continuous outcomes
• Selection plots used to inspect coefficients across penalty values
• Cross-validation folds configured for stable penalty parameter selection
• Postestimation predictions generated for validation and test observations
• Root mean squared error compared across competing regression models
• Residual diagnostics reviewed for systematic prediction errors and extremes

Participants can use elastic net when predictor correlation affects lasso variable selection.

• Elastic-net penalties combine lasso selection with ridge shrinkage
• Alpha values compared across correlated financial and economic predictors
• Elasticnet linear estimated using candidate alpha and lambda grids
• Grouped predictors assessed for stability across resampled training datasets
• Coefficient paths reviewed before selecting the final penalty specification
• Model parsimony balanced against predictive accuracy and interpretability requirements

Participants can develop lasso logistic models for binary risk and event predictions.

• Lasso logit estimated for default, fraud, churn, and event outcomes
• Predicted probabilities generated using predict after model estimation
• Classification thresholds selected from decision costs and operational capacity
• Confusion matrices calculated for true positives, false positives, and errors
• Sensitivity, specificity, precision, and recall compared across thresholds
• Class imbalance addressed through sampling decisions and appropriate performance metrics

Participants can assess discrimination and probability calibration for classification models.

• Receiver operating characteristic curves plotted across classification thresholds
• Area under the curve interpreted as ranking discrimination
• Precision-recall curves applied to rare-event classification problems
• Calibration plots compare predicted probabilities against observed event rates
• Brier scores measure probability forecast accuracy and calibration
• Decile lift tables identify concentrated risk and intervention segments

Participants can select suitable machine learning approaches for count and nonlinear outcomes.

• Lasso poisson fitted for claims, transactions, incidents, and visits
• Exposure offsets incorporated into count models with differing observation periods
• Zero inflation assessed before interpreting sparse count prediction results
• Nonlinear terms specified with splines, polynomials, and interaction features
• Generalised linear model benchmarks compared with penalised count specifications
• Prediction intervals discussed separately from point-prediction accuracy measures

Participants can design validation procedures that provide credible performance estimates.

• K-fold cross-validation estimates average performance across resampled partitions
• Repeated cross-validation reduces dependence on a single random split
• Bootstrap resampling estimates uncertainty in model performance statistics
• Grouped folds prevent related customers or firms crossing sample boundaries
• Time-series validation preserves information timing and forecast origin
• Nested validation separates hyperparameter tuning from final performance assessment

Participants can assess tree-based models alongside regularised regression models.

• Decision trees partition observations using recursive binary splitting rules
• Tree depth, minimum nodes, and pruning control model complexity
• Random forest principles compared with single-tree prediction behaviour
• Stata-compatible community packages assessed for installation and reproducibility
• Variable importance measures interpreted cautiously with correlated predictors
• Lasso and tree-based results compared using identical test samples

Participants can distinguish predictive modelling from high-dimensional causal estimation.

• Predictive accuracy differs from unbiased treatment-effect estimation objectives
• Double-selection lasso identifies controls for treatment-effect regression models
• Dsregress estimates treatment effects with high-dimensional candidate controls
• Partialling-out methods reduce regularisation bias in causal coefficient estimation
• Confounder timing assessed before specifying treatment and control variables
• Causal claims limited by design, identification assumptions, and data quality

Participants can communicate model outputs, limitations and recommended actions to stakeholders.

• Prediction drivers communicated without overstating causal interpretation or importance
• Partial dependence concepts explained for nonlinear predictor-response relationships
• Segment-level performance reviewed by portfolio, geography, and customer category
• Error analysis identifies cases requiring manual review or additional data
• Decision thresholds linked to costs, benefits, capacity, and risk appetite
• Executive model summaries present purpose, performance, limitations, and actions

Participants can prepare a governance and monitoring plan for a Stata prediction model.

• Model inventory entries record owner, purpose, data, method, and usage
• Validation reports document sample design, metrics, results, and limitations
• Data drift monitored through distributions, missingness, and population stability
• Performance drift tracked using realised outcomes and scheduled recalibration reviews
• Change-control procedures govern data, code, threshold, and model revisions
• Deployment plans specify scoring frequency, outputs, users, and escalation routes

Participants can build, review and present a complete documented Stata machine learning analysis for a business or policy prediction problem.

• Business prediction question framed with target, decision, and success metric
• Analytical dataset prepared with documented transformations and feature definitions
• Baseline and regularised models estimated using reproducible Stata do-files
• Validation evidence compares predictive accuracy, calibration, and operational thresholds
• Model-risk review records limitations, drift indicators, and governance controls
• Stakeholder presentation recommends model use, monitoring, and next implementation steps