Distribution Interpretation And Data Diagnostics

What's being tested

These tasks test practical data diagnostics and distribution interpretation skills an MLE must use to turn raw signals into reliable training data and actionable model decisions. Interviewers look for safe framing (regression vs classification), robust preprocessing for JSON/log-style inputs, baseline modeling choices, and the ability to detect and explain distributional differences and class imbalance. At Reddit scale the focus is operational: detect bad inputs, avoid label leakage, quantify shift, and propose deployable mitigations rather than theoretical proofs.

Core knowledge

• JSON/schema validation: validate nested JSON with a schema (e.g., jsonschema), enforce types, and cast early so downstream training pipelines receive deterministic types and shapes.

• Missingness taxonomy: distinguish MCAR/MAR/MNAR; decide imputation vs indicator variables; dropping rows is acceptable only when missingness rate << 1% or the rows are non-representative.

• Categorical encoding: for high-cardinality categorical features use target encoding (with smoothing/regularization) or hashed embeddings; reserve one-hot for low-cardinality (< 20) only.

• Class imbalance and sampling: measure imbalance ratio $r = n_{minority}/n_{majority}$; prefer metric-aware strategies (class-weighting, focal loss, stratified sampling) over naive resampling that can leak temporal structure.

• Problem framing: ask whether predicting y is regression (continuous target) or classification (discrete/thresholded), and pick baseline metrics: $RMSE$ or $R^2$ for regression, AUC, precision-recall, and calibration for classification.

• Baseline models and metrics: always establish a simple baseline: global mean/median, logistic regression, or XGBoost with default params; compare improvements to these on holdout and on slices.

• Distribution comparison tests: use KS test for continuous features, chi-square or Cramér's V for categoricals, and visualize with overlapping histograms, ECDFs, or population-normalized bar charts to spot subtle shifts.

• Label leakage and target shift: look for upstream features computed using future info; detect via feature importance that suddenly dominates or by retraining with feature holdouts.

• Temporal splits and offline/online parity: use time-based train/validation/test splits for streaming data, simulate production sampling, and check parity metrics between offline features and live-serving features.

• Calibration and reliability: evaluate predicted probabilities with Brier score and calibration plots, and consider isotonic/logistic calibration on holdout if probabilities drive downstream decisions.

• Cold-start handling: define strategies for new users/items: population priors, user/item embeddings initialized from content features, or fallback to business-rule models until enough data accrues.

• Monitoring and drift detection: instrument pipelines to compute per-feature population statistics and alert on changes (mean, std, null rate, cardinality), and consider PSI (Population Stability Index) thresholds for action.

Worked example — "Load and prepare JSON for modeling"

First 30 seconds: confirm the JSON sources (event stream vs daily batch), expected record volume, whether nested fields or arrays exist, and whether target label is present in the same document. A strong candidate states assumptions: "I'll assume each event is one row; timestamps exist and are in UTC." Skeleton answer pillars: (1) schema validation and coercion using jsonschema + typed pandas dtypes; (2) flattening and normalization for nested keys and consistent field names; (3) missing-value strategy per-field (impute, indicator, or drop) with rationale; (4) encoding for categoricals (one-hot vs target vs hashing); (5) data-splitting using time-aware holdouts and initial sanity checks (counts, unique cardinalities, class balance). A key tradeoff to flag: aggressive imputation can hide upstream bugs, so prefer adding missingness indicators and logging raw rates rather than blind mean-imputation. To close: propose quick unit tests, dataset-level assertions, and if time permits, add Great Expectations checks and a small end-to-end smoke test comparing offline features to a live sample.

A second angle — "Model y from x and interpret distributions"

When shifting to modeling and distribution interpretation, emphasize framing the task (binary vs continuous), choosing appropriate metrics (AUC vs precision@k for imbalanced classes, RMSE vs MAE for outliers), and interpreting feature distribution differences across cohorts. If constraints tighten — for example, extremely limited labeled data — discuss transfer learning, stronger regularization, and how distributional differences imply a need for domain adaptation methods or reweighting. Also highlight calibration: improved AUC doesn't guarantee well-calibrated probabilities needed for downstream ranking or UX decisions. Finally, for cold-start, explicitly outline fallbacks: priors, content-based features, and quick online learning loops.

Common pitfalls

Pitfall: Treating missing as zero by default.
Assuming zeros are neutral will bias models if missingness carries meaning. Instead, record missingness explicitly, inspect distributions conditioned on missingness, and use appropriate imputation or indicator strategies.

Pitfall: Not clarifying objective/regression vs classification.
Jumping straight to algorithms without asking whether the business needs calibrated probabilities, ranking, or point estimates leads to wrong metrics and bad baseline choices. State the objective, metric, and cost of different error types.

Pitfall: Overfitting to training distribution and ignoring drift.
A model that looks great offline but fails in production often resulted from optimistic sampling or feature leakage; demonstrate time-based validation, slice analysis, and monitoring plans to show operational readiness.

Connections

This topic commonly leads to pivots on feature stores and serving parity (feast, ingestion consistency), model monitoring (drift detectors and alerting), and A/B experimentation considerations when model outputs influence upstream data generation. Being able to link diagnostics to deploy/test/monitor cycles is essential.

Further reading

• Technical Debt in Machine Learning Systems — Sculley et al., 2015 — practical taxonomy of production ML pitfalls and data issues.