Predictive Assessment Techniques

📊How accurately can we predict turnover and workers’ comp claims a year in advance? Turnover and workers' comp claims are costly for organisations and difficult experiences for employees. Knowing where risk is likely to emerge gives HR and Health & Safety teams a chance to proactively manage it. But how accurately can these outcomes be predicted in advance? To explore this, we trained a gradient-boosted decision tree model on data from the Household, Income, and Labour Dynamics in Australia survey (2001–2023), which included 191,000 observations from nearly 25,000 workers. We used predictors that mirror what most HR systems or engagement surveys capture including demographics, tenure, role characteristics, compensation, benefits, and job satisfaction. We trained on 80% of the workers and tested on the remaining 20%. What we found: 🎯 Triple the Accuracy for the Highest-Risk Individuals: The top 3% flagged were 3.5× more likely to actually leave or claim than a random 3%. 🔬Double the Overall Prediction Quality: Across the whole workforce, the model was over twice as good as chance at separating higher- from lower-risk employees. 🔍 Concentrated Risk for Intervention: The top 10% flagged accounted for nearly 3× more cases than expected by chance. What this means: Even a year in advance, a data-driven approach can provide a strong signal to help focus retention and safety efforts. The accuracy, while not perfect, is high enough to be useful, especially when a model like this is used to support the expertise of managers, organisational psychologists, and other specialists. It can help HR and Health & Safety teams develop proactive and targeted risk management efforts. The exciting thing is that this was all with broad, national survey data. With higher-quality internal data from a single organisation, predictive accuracy could be even stronger. But the challenge is making sure the right data is being collected and shared between units and systems, which is often the hardest part of turning analytics into action. #PeopleAnalytics #PredictiveAnalytics #EmployeeTurnover #HRTech #MachineLearning #WorkplaceSafety #DataScience #HR

Bayesian logistic regression is a powerful method for predicting binary outcomes (such as yes/no decisions). It differs from traditional logistic regression by incorporating prior beliefs and quantifying uncertainty using posterior distributions. This makes Bayesian logistic regression ideal for situations where you want to explicitly account for uncertainty or include prior knowledge. Here’s a breakdown of the four key graphs that provide insights into a Bayesian logistic regression model: ✔️ Posterior Distribution Plot: This plot displays the posterior distributions of the coefficients for predictor1 and predictor2. The shaded area shows the range of probable values (credible intervals), while the vertical line marks the median estimate of each coefficient. Unlike frequentist approaches that provide single point estimates, Bayesian logistic regression gives a distribution of possible values, which allows for a clearer understanding of uncertainty in the model parameters. ✔️ Trace Plot: This shows the trace of the MCMC (Markov Chain Monte Carlo) sampling process over 4000 iterations for predictor1 and predictor2. The traces should ideally look "fuzzy" and well-mixed, moving around the full parameter space without getting stuck. This indicates that the chains have converged and that the model’s parameter estimates are reliable. A poorly mixing chain (one that looks like a straight line or is stuck) would indicate convergence issues. ✔️ Posterior Predictive Check: This plot helps to evaluate the model's predictive performance by comparing the predicted outcomes (y_rep, light blue) with the observed data (y, dark blue). The closer the predicted values align with the observed data, the better the model captures the underlying structure. In this case, the predicted values align well with the observed data, indicating a good fit. This check is crucial for assessing whether the model generates realistic predictions. ✔️ Posterior Interval Plot: This plot visualizes the credible intervals for the model coefficients, including the intercept. The wider the credible interval, the more uncertainty there is in that coefficient estimate. Both 50% (inner) and 95% (outer) credible intervals are shown, providing a range of probable values for each coefficient. If a credible interval includes zero, it means the predictor may not have a strong effect on the target variable. This grid of graphs allows for a comprehensive understanding of your Bayesian model, showing how well the model fits the data and how much uncertainty there is in the parameter estimates. Bayesian logistic regression provides a richer interpretation than traditional methods by quantifying uncertainty and incorporating prior knowledge into the analysis. Want more insights on data science? Subscribe to my free email newsletter! Further details: http://eepurl.com/gH6myT #statistics #research #visualanalytics

The notes provide an introduction to predictive modeling, focusing on statistical models and practical applications using R. The course emphasizes understanding the intuition behind different predictive methods and applying them to real datasets. The concept of predictive modeling is introduced as creating mathematical models to predict a variable of interest Y based on predictor variables X1,…,Xp. Examples include predicting house prices, or failure probabilities. The general modeling framework is Y = m(X1, …,Xp) + \epsilon, where m is the unknown regression function and \epsilon is the random error. Several key considerations are highlighted: 1. Prediction Accuracy vs. Interpretability: Models can be complex and accurate but difficult to interpret (e.g., black-box models) or simpler and interpretable with potentially lower accuracy. 2. Model Correctness vs. Usefulness: A model may be theoretically correct but not practically useful, or vice versa. 3. Flexibility vs. Simplicity: There is a trade-off between underfitting and overfitting, addressed using training/testing splits and the bias–variance trade-off. The document covers: • Linear Models (Simple & Multiple): Foundational tools for modeling linear relationships, parameter estimation using least squares, and interpretation of coefficients. • Model Selection & Diagnostics: How to choose predictors, handle categorical variables, capture nonlinearities, and check assumptions. • Advanced Linear Techniques: Shrinkage methods (e.g., Ridge, Lasso), constrained models, multivariate responses, and considerations for big data. • Generalized Linear Models (GLMs): Extending linear models to handle non-normal response variables, including logistic regression, deviance, and model selection. • Nonparametric Regression: Flexible methods like kernel regression and density estimation for situations where functional forms are unknown. Practical aspects include using R and RStudio, running reproducible code snippets, and working with provided datasets such as Boston housing prices, and the Challenger disaster data. Appendices review hypothesis testing, estimation techniques, multinomial logistic regression, and handling missing data. Link: https://lnkd.in/eNBedNnB #statistics #predictivemodeling #r

From Raw Sensor Data to Reliable Maintenance Predictions Industrial equipment doesn't fail without warning—but spotting those early signs requires more than intuition. In this post, I combine statistical methods, PCA, and deep learning to show how time series analysis can deliver real predictive maintenance power. I walk through a complete pipeline to 1/Clean and normalize multivariate time series data, 2/Use Principal Component Analysis to reduce noise and spot outliers, 3/Apply statistical baselines to define “normal” operation, and 4/Train an LSTM model to forecast future behavior and flag deviations The key idea is to build health metrics that are more flexible than standard control charts. combine interpretable metrics like PCA with the predictive strength of LSTMs to catch failures early—sometimes before the first visible signs. This article includes Python code, plots, and a real-world dataset from NASA’s turbofan engine simulations. If you're building predictive maintenance systems or working with time series in any domain, this walkthrough shows how classic techniques and neural networks can work together. https://lnkd.in/gEEeQEV8

 No one-size-fits-all in demand forecasting. This document shows 21 forecasting techniques for planners: 1️⃣ Naive Forecast ↳ “Tomorrow = Today”; best for highly stable, low-variability SKUs 2️⃣ Moving Average ↳ Calculates average demand over a fixed window; smooths noise but lags behind trends or seasonality 3️⃣ Weighted Moving Average ↳ Gives more weight to recent periods; useful when recent trends are more relevant than older data 4️⃣ Simple Exponential Smoothing ↳ Forecasts using a smoothing constant (alpha) to weight recent demand more heavily; best for flat, non-seasonal data 5️⃣ Holt’s Linear Trend Method ↳ Builds trend into exponential smoothing; suitable for items with consistent upward or downward movement 6️⃣ Holt-Winters (Triple Exponential Smoothing) ↳ Adds seasonality on top of level and trend; ideal for seasonal SKUs 7️⃣ Linear Regression ↳ Finds a straight-line relationship between a dependent variable (e.g., sales) and one independent factor (e.g., price) 8️⃣ Multiple Linear Regression ↳ Accounts for several demand drivers at once (promotions, discounts); good for mature categories with complex dynamics 9️⃣ ARIMA (AutoRegressive Integrated Moving Average) ↳ Great for time-series data with trends and autocorrelation 1️⃣0️⃣ SARIMA (Seasonal ARIMA) ↳ Adds a seasonal component to ARIMA; helpful when monthly or quarterly patterns repeat reliably 1️⃣1️⃣ Transfer Function Models ↳ Combine ARIMA with external input variables (e.g., advertising spend or GDP); useful for planning with known economic factors 1️⃣2️⃣ XGBoost / LightGBM ↳ Powerful tree-based algorithms; handles outliers, nonlinear relationships, and multiple variables 1️⃣3️⃣ Random Forest ↳ Builds multiple decision trees and averages the outputs; reduces overfitting and works well with many predictors 1️⃣4️⃣ Neural Networks ↳ Mimics the human brain; excellent at capturing nonlinear, complex relationships 1️⃣5️⃣ Prophet (by Meta/Facebook) ↳ Designed for business users; automatically detects trends, holidays, and seasonality 1️⃣6️⃣ LSTM (Long Short-Term Memory Networks) ↳ A type of deep learning specifically for sequences; excellent at modeling long-term dependencies in time series 1️⃣7️⃣ Support Vector Regression ↳ Effective for high-dimensional, noisy datasets; less popular than others, but still powerful in niche applications 1️⃣8️⃣ Expert Judgment ↳ Relies on domain knowledge when data is unreliable or missing (e.g., for new products or crisis situations) 1️⃣9️⃣ Delphi Method ↳ Structured technique using rounds of anonymous expert feedback until consensus is reached; great for strategic forecasts 2️⃣0️⃣ Sales Force Composite ↳ Structured technique using rounds of anonymous expert feedback until consensus is reached; great for strategic forecasts 2️⃣1️⃣ Consensus Forecasting ↳ Final demand plan formed through cross-functional alignment (demand, supply, finance) in the S&OP process Any others to add?

*** How to Choose and Validate a Predictive Model *** Choosing a Predictive Model 1. **Define the Objective** - Clarify your prediction goal (e.g., classification vs. regression). - Identify the business or research objective behind the prediction. 2. **Understand Your Data** - Assess the size, quality, and data type (structured vs. unstructured). - Evaluate missing values and distributions, and identify potentially important features. 3. **Consider Model Complexity** - Simple models (e.g., linear regression, decision trees) are easier to interpret. - Complex models (e.g., random forests, neural networks) may provide higher accuracy but less transparency. 4. **Balance Bias and Variance** - Aim to avoid underfitting (high bias) and overfitting (high variance). - Utilize learning curves to diagnose model performance. 5. **Align with Resources** - Some models require more computational power or expertise for deployment and maintenance. Validating a Predictive Model 1. **Train/Test Split** - Divide the data into training and testing sets (e.g., 70% training and 30% testing) to estimate performance on unseen data. 2. **Cross-Validation** - Use k-fold cross-validation to reduce evaluation variance and improve model generalizability. 3. **Performance Metrics** - For classification: measure accuracy, precision, recall, F1-score, and AUC-ROC. - For regression: use RMSE, MAE, and R². 4. **Hyperparameter Tuning** - Employ grid search, random search, or Bayesian optimization to fine-tune model parameters. 5. **Model Interpretation** - Utilize tools like SHAP, LIME, or partial dependence plots to build trust and gain insights into the model’s decisions. --- B. Noted

The future of culture analytics isn't just measuring what happened. It's predicting what will happen and prescribing what should happen next. Most HR analytics remain stubbornly retrospective—reporting on past engagement scores, historical turnover, or completed training. This backward-looking approach limits HR's strategic impact. The most advanced culture-first tech stacks are now incorporating three progressive levels of analytics: 1. Predictive Analytics: Using historical patterns to forecast future outcomes • Flight risk prediction based on engagement trends and manager interactions • Performance trajectory forecasting based on learning activity and feedback patterns • Team effectiveness projections based on collaboration metrics and skill distribution 2. Prescriptive Analytics: Recommending specific interventions based on predicted outcomes • Targeted retention strategies for high-risk, high-value talent • Personalized development recommendations to address emerging skill gaps • Team composition suggestions to optimize collaboration and innovation 3. Adaptive Analytics: Systems that learn from intervention results to continuously improve recommendations • Tracking which culture initiatives most effectively address specific challenges • Identifying which manager behaviors most consistently improve team engagement • Quantifying the ROI of different approaches to recognition, development, and communication Organizations implementing these advanced capabilities are transforming HR from a reactive function to a predictive force that shapes business outcomes through precisely targeted culture interventions. The technology to enable this transformation exists today—the question is whether your organization is ready to embrace it. ♻ Repost if you found this insightful 📣 Follow me, Anthony Calleo, for EX insights 🌐 Contact Calleo EX for a free consultation #EmployeeExperience #EX #CalleoEX #WorkplaceCulture #HumanResources #EmployeeEngagement #DataDrivenCulture #DataDrivenLeadership

Every policy proposal is a prediction about how the future will transpire; understanding the future is thus a central task for policymakers. This new report from fp21's Thomas Scherer offers a first-of-its-kind review of the nascent body of evidence of what works in accurately forecasting the future. It explains what works, and why, and jumpstarts a necessary conversation about how to implement these best practices within the hallways of government. Every foreign policy expert should familiarize themselves with these cutting-edge approaches. (And certainly its lessons travel far beyond the realm of international relations) There has been an explosion of new methods and research in recent years to help people more accurately grasp the future. It turns out that some of these approaches are backed by great evidence, while some may be little more than snake oil. The report organizes common prediction tools into three categories, each informed by a different body of research: 🧠Human Judgment Forecasting involves developing highly accurate human predictors by identifying talent, training skills, and refining the prediction process. 📈Quantitative Prediction involves using datasets and statistical methods to model relationships between variables and extrapolate them to make predictions. 🔭Foresight (also known as futurism or horizon scanning) involves scanning for signals and trends of possible futures and then working through scenarios based on those futures to improve planning in the present. The report finds robust evidence to suggest Human Judgment Forecasting and Quantitative Prediction are valuable tools. However, there is little evidence to demonstrate the efficacy of Foresight methods, despite their growing popularity in the private sector. The lack of evidence for Foresight is a yellow light, not a red one — the field has demonstrated a disinterest in evaluating the efficacy of its approaches, and more investment is needed here (and across the other two categories as well) to better understand how these tools work. "The question we care most about is the extent to which improved forecasting and prediction can improve the effectiveness of a policy’s impact in the real world. Like the Greek myth of Cassandra, knowing the future but being cursed to be ignored is unhelpful.” Thomas thus challenges our government to think hard about how to build these tools into the policy process. As always, I’m eager to learn more from our readers on this juicy topic: ❓Have you used forecasting and prediction tools in your work? ❓Are there any methods or research we missed? ❓Have you discovered ways to effectively integrate these tools into decision-making? Check out the full report here: https://lnkd.in/e6f274aB

𝐐𝐀 𝐭𝐞𝐚𝐦𝐬: are you relying on instinct to decide which tests to prioritize? 😕 That method can quietly drain your time and leave high-risk areas exposed. Many teams treat test coverage like a numbers game. More tests must mean better quality, right? But here’s the reality… 𝘚𝘰𝘮𝘦 𝘵𝘦𝘴𝘵𝘴 𝘯𝘦𝘷𝘦𝘳 𝘧𝘢𝘪𝘭. 𝘚𝘰𝘮𝘦 𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 𝘢𝘭𝘸𝘢𝘺𝘴 𝘣𝘳𝘦𝘢𝘬 𝘢𝘧𝘵𝘦𝘳 𝘶𝘱𝘥𝘢𝘵𝘦𝘴. 𝘈𝘯𝘥 𝘴𝘰𝘮𝘦 𝘢𝘳𝘦𝘢𝘴 𝘤𝘢𝘶𝘴𝘦 𝘪𝘴𝘴𝘶𝘦𝘴 𝘳𝘦𝘱𝘦𝘢𝘵𝘦𝘥𝘭𝘺 𝘺𝘦𝘵 𝘨𝘦𝘵 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘢𝘴 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘦𝘭𝘴𝘦. Predictive analytics helps shift that dynamic. By pulling data from failed tests, bug histories, and past releases, you start to see patterns, the features that break more often, the types of changes that introduce risk, and the areas that need closer inspection. You can: ➡️ Focus testing on modules that are statistically more likely to fail ➡️ Surface high-risk code paths earlier in the cycle ➡️ Reduce noise by identifying tests that rarely catch defects When you understand what’s likely to go wrong, you don’t have to treat every test like it’s equal. The data is already telling a story. It’s just a matter of paying attention. 🚀 #QA #SoftwareTesting #PredictiveAnalytics