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Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating mo...
Builds diagnostic and prognostic classifiers on omics feature matrices with regularized logistic regression, random forest, and gradient-boosted trees, handling the p>>n regime, batch shortcut learning, class imbalance, and probability calibration. Use when building a classifier from expression, ...
End-to-end biomarker discovery workflow from expression data to validated biomarker panels. Covers feature selection with Boruta/LASSO, leakage-safe cross-validation, calibration, and SHAP interpretation. Use when building and validating diagnostic or prognostic biomarker signatures from omics data.
Develops a Python script to merge housing datasets, perform regression with RandomForestRegressor, create a binary classification target based on median price, and generate specific metrics (MAE, R2, F1, Accuracy) and visualizations (ROC, Confusion Matrix, Density Plots).
Build robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pipeline automation
Generates Python code to predict safe spots in a 5x5 Minesweeper grid using KMeans clustering on historical data, ensuring unique, deterministic, and reproducible results.
Machine learning pipeline for scientific research including data preprocessing, feature engineering, model selection, training, evaluation, and interpretation. Covers supervised/unsupervised learning, deep learning, cross-validation, hyperparameter tuning, and model explainability. Use when user ...
Apply NLP and text mining techniques to research text data
Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
Build tensorflow model trainer operations. Auto-activating skill for ML Training. Triggers on: tensorflow model trainer, tensorflow model trainer Part of the ML Training skill category. Use when working with tensorflow model trainer functionality. Trigger with phrases like "tensorflow model train...
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