K Knowledge HF-D7.7-004 cross-sector 9 skills
Machine learning fundamentals
Knows the main forms of machine learning, the role of training data and concepts such as overfitting, validation and model performance in plain language.
How hrmforce measures this
- Assessment method
- Knowledge test · rho 0.40 (SD 0.13)
- hrmforce instrument
- Knowledge test (client-specific)
- Trainability
- high
- Demand outlook 2026 to 2030
- rising
Test of declarative job knowledge, usually assembled per client.
Behavioural anchors
| Level | Behaviour at this level |
|---|---|
| N1 Guided | States the difference between supervised and unsupervised learning and which data a model needs to learn. works under supervision and follows instruction · routine, one variable at a time · own task |
| N3 Proficient | Explains how a model is trained and validated and judges whether reported performance figures are credible. sets own approach and seeks input proactively · several variables, some ambiguity · own team or process |
| N5 Leading | Sets requirements for model development in the organisation and reviews third party models on design and evidence. sets the standard and the policy · strategic, under high uncertainty · organisation, value chain or profession |
N2 and N4 are deliberately not anchored. Raters place them between the anchors, following the O*NET convention.
Underlying skills
These skills inherit the assessment route and the behavioural anchors of this construct.
| T | Skill | Definition | Demand outlook 2026 to 2030 |
|---|---|---|---|
| K | Knowing the main forms of machine learning Supervised learning · Machine learning | Distinguishes supervised, unsupervised and reinforcement learning and names a practical example of each. | rising |
| K | Splitting training and validation sets Train test split · Cross validation | Splits data into training, validation and test parts so model performance is measured on unseen data. | rising |
| K | Recognising overfitting Overfitting | Recognises that a model memorises the training data and therefore performs poorly on new cases. | rising |
| K | Measuring model performance Precision · Recall · Model evaluation | Chooses suitable measures such as precision, recall and area under the curve and explains what they mean. | rising |
| K | Distinguishing classification and regression Classification · Regression | Determines whether a problem asks for a class or a number and chooses the matching model type accordingly. | rising |
| V | Engineering features Feature engineering | Turns raw data into usable predictive variables and prevents future information leaking into the model. | rising |
| K | Knowing neural networks at a high level Neural networks · Deep learning | Explains how layers, weights and training relate and why such models are hard to explain. | rising |
| V | Building an evaluation set Evaluation set · Benchmark | Assembles a fixed set of test cases with expected answers to assess models and prompts comparably. | rising |
| K | Understanding model explainability Explainable AI | Explains which techniques show which variables drive a prediction and what their limitations are. | rising |