Provides data preprocessing visualization and statistical correlation methods. Use when the user asks to visualize feature distributions, wants to plot correlation matrices or dendrograms, explore feature interactions, or needs to calculate mutual information or label-based statistics in a Python data science project using sklearn-compatible models.
Provides evaluation metrics and visualization charts for machine learning models. Use when the user asks to evaluate a model, wants to plot a confusion matrix, ROC curve, or Precision-Recall curve, needs to analyze learning curves, probability distributions, or error analysis in a Python data science project using sklearn-compatible models.
Sklearn pipeline transformers for multi-label binarization and sentence embeddings. Use when the user needs MultiLabelBinarizer in a Pipeline, ColumnTransformer, or set_output(transform="pandas"), or when integrating sentence-transformers models into sklearn pipelines for NLP tasks.
Provides Explainable AI (XAI) tools to interpret machine learning models. Use when the user asks to visualize feature importance for tree-based models, needs to explain which features drive model decisions, wants to render or draw a decision tree diagram, or needs to convert a Graphviz DOT string into a matplotlib figure in a Python data science project using sklearn-compatible models.
Provides string manipulation and text analysis functions for DataFrames. Use when the user asks to one-hot encode comma-separated tags into boolean columns, or needs to extract statistically significant terms from a subset of text documents in a Python data science project.
Provides evaluation and visualization for unsupervised learning and clustering. Use when the user asks to plot cluster cardinality, wants to visualize cluster magnitude, compare magnitude versus cardinality to find anomalies, or needs to determine the optimal number of clusters in a Python data science project using sklearn-compatible models.