voorspellen-classificatie

name

voorspellen-classificatie

description

Bouw voorspelmodellen voor categorische uitkomsten (ja/nee of meerdere klassen) waar per individu een correcte voorspelling telt — bijv. "voorspel of een student uitvalt", "classificeer aanvragen als goedgekeurd/afgekeurd", "predict churn yes/no". Gebruik deze skill bij elke classificatietaak. LET OP — als het doel is een lijst te sorteren op risico en de top-N te selecteren (beperkte capaciteit, uitnodigingslijst, prioritering), gebruik dan de skill voorspellen-ranking in plaats van deze.

Prediction: classification

Goal: predict a class per row (dropout yes/no, category A/B/C). Evaluated on the quality of individual predictions. The user is non-technical: address them in Dutch and keep explanations in plain language.

Workflow

When the user invokes /voorspellen-classificatie [optional: file path or target variable]:

Step 0 — Check: is this actually classification?

Two checks before you start:

1. Ranking? For a binary target, always ask first: is this about hard yes/no decisions per individual, or about sorting by risk and selecting the top-N (e.g. who to invite under limited capacity)? In the second case: use the skill /voorspellen-ranking. Classic classification optimises a decision boundary at 0.5 and ignores the ordering within groups; for prioritisation lists that is the wrong optimisation.
2. Time series? Is this about predicting future events over time where rows are timestamps (e.g. "will there be a peak next month")? Then this is forecasting, not row-based classification — say so and use a forecasting approach. For data across multiple years (panel data): split on time (train on earlier years, test on the most recent year) instead of random.

Step 1 — Intake (always ask first, do not assume)

Ask these in plain Dutch. Skip a question only if the answer is already in the conversation.

1. Interpretable or black box?
   • Interpretable = you can explain why (e.g. logistic regression, Lasso-logistic, small decision tree, Random Forest) often appropriate for education.
   • Black box allowed = often more accurate (e.g.g Gradient Boosting, XGBoost, SVM).
2. Automatically fine-tune settings or use default settings? (ask this in plain language, not with terms like hyperparameters or cross-validation)
   • Default = fast, fine first version.
   • Fine-tune = the model tries many combinations of settings and picks the best. Takes a lot longer. Technical detail: see references/hyperparameters.md.
3. Benchmark multiple algorithms or one recommendation?
   • Benchmark = train all suitable algorithms, one comparison table (e.g. cedanl/uitnodigingsregel-benchmark approach: RF, Lasso/LogReg, SVM, Gradient Boosting, XGBoost).
   • Recommendation = llm assess the case and recommend one algorithm with a short rationale. Weigh: number of rows (logistic regression strong with little data; boosting with lots of data), class balance, many categorical features (trees), expected non-linearity, and the interpretability requirement from question 1. Do not hardcode a fixed default.

Step 2 — Data

If the data is not yet prepared, first follow the skill /voorspellen-dataprep (overview, leakage check, missings, encoding, split). Core, also if that skill is unavailable:

• Split 80/20 stratified on the target column (random_state=42) before preprocessing; everything in an sklearn Pipeline (no leakage).
• Impute missings (median/mode), one-hot with handle_unknown="ignore", StandardScaler only for logistic regression and SVM.
• Check class balance. For a skewed distribution (< ~20% minority class): report it, use class_weight="balanced" where possible and do not rely on accuracy.

Step 3 — Algorithms

Algorithm	Interpretable	Scaling
Logistic regression	yes	yes
Small decision tree	yes	no
Random Forest	yes	no
Gradient Boosting	no	no
XGBoost	no	no
SVM (rbf, probability=True)	no	yes

When tuning: use the grids from references/hyperparameters.md (scoring: "f1" or "roc_auc" for imbalance). Choose the search method yourself (it is described in that file); do not bother the user with it.

Step 4 — Evaluation and reporting

Report on the test set: accuracy, precision, recall, F1 (per class for multiclass), confusion matrix, and ROC-AUC for a binary outcome. Give a baseline (majority class) for context.

For a benchmark: one table, sort by F1 (or AUC for imbalance), name the winner.

For interpretable models: show coefficients/odds ratios. For ensembles: feature importances, with the caveat that this is not causality.

Deliver: (1) a summary in plain Dutch, (2) reproducible code, (3) a joblib model on request.

Important

• Goal is to sort by risk and select the top-N? Use /voorspellen-ranking, not this skill.
• Data not yet prepared? First /voorspellen-dataprep.
• Intake (Step 1) always ask first — do not assume; do not hardcode a fixed default algorithm.
• Always split before preprocessing, everything in an sklearn Pipeline (no leakage).
• The user is non-technical: all output and questions to the user are in Dutch; explain choices in plain language, no jargon.