by K-Dense-AI
scikit-survival skill for survival analysis and time-to-event modeling in Python. Use this guide for censored data, Cox models, random survival forests, gradient boosting, Survival SVMs, and survival metrics like concordance index and Brier score.
by K-Dense-AI
scikit-learn helps you build classical machine learning workflows in Python. Use this scikit-learn skill for classification, regression, clustering, preprocessing, model evaluation, hyperparameter tuning, and pipelines. It’s a practical scikit-learn guide for tabular data and repeatable model development.
by K-Dense-AI
molfeat is a molecular featurization skill for ML and Data Analysis. It helps convert SMILES or RDKit molecules into fingerprints, descriptors, and pretrained embeddings for QSAR, virtual screening, similarity search, and chemical space analysis. Use this molfeat guide to pick practical representations and build reusable featurization pipelines.
by K-Dense-AI
geniml is a skill for genomic interval machine learning on BED files, scATAC-seq outputs, and chromatin accessibility data. Use it for Region2Vec, BEDspace, scEmbed, consensus peaks, and other region-level ML workflows. It is a good fit when you need embeddings, clustering, or preprocessing guidance for genomic regions.
by K-Dense-AI
aeon is a scikit-learn-compatible Python skill for time series machine learning. Use it for classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, and other temporal data workflows. It fits univariate and multivariate analysis when you need specialized methods beyond generic tabular ML.
