Agent skill · research science · aj-geddes
Classification Modeling
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Why this skill is useful
Provides specific implementation details and code examples for various classification algorithms that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, seaborn installed locally. About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Classification Modeling Overview Classification modeling predicts categorical target values, assigning observations to discrete classes or categories based on input features. When to Use Predicting binary outcomes like customer churn, loan default, or email spam Classifying items into multiple categories such as product types or sentiment Building credit scoring models or risk assessment systems Identifying disease diagnosis or medical condition from patient data Predicting customer purchase likelihood or response to marketing Detecting fraud, anomalies, or quality defects in production systems Classification Types Binary Classification: Two classes (yes/no, success/failure) Multiclass: More than two classes Multi-label: Multiple classes per observation Common Algorithms Logistic Regression: Linear classification Decision Trees: Rule-based non-linear Random Forest: Ensemble of decision trees Gradient Boosting: Sequential tree building SVM: Support Vector Machines Naive Bayes: Probabilistic classifier Key Metrics Accuracy: Overall correct predictions Precision: True positives / (true + false positives) Recall: True positives / (true + false negatives) F1-Score: Harmonic mean of precision/recall AUC-ROC: Area under receiver operating characteristic curve Implementation with Python Class Imbalance Handling Oversampling: Increase minority class samples Undersampling: Reduce majority class samples SMOTE: Synthetic minority oversampling Class weights: Penalize misclassifying minority class Threshold Selection Default (0.5): Equal misclassification cost Custom threshold: Based on business requirements Optimal: Maximizing F1-score or AUC Deliverables Classification metrics (accuracy, precision, recall, F1) Confusion matrices for all models ROC and Precision-Recall curves Feature importance analysis Model comparison table Recommendations for best model Probability calibration plots
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/classification-modeling