idanmoradarthasCommunity·6 Agent Skills Included

DataScienceUtils

Model evaluation charts, clustering analysis, and feature statistics

Generates confusion matrices, ROC and Precision-Recall curves, learning curves, and error analysis reports for sklearn models. Visualizes feature distributions, correlations, cluster quality, and decision tree importance without writing boilerplate plotting code. Includes pipeline-ready transformers for multi-label encoding and sentence embeddings to speed up data science workflows.

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npx skills add idanmoradarthas/DataScienceUtils --all -g -y

All Skills in This Repository (6)

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📦 In Repo

idanmoradarthas

ds-utils-metrics

Analyze and visualize sklearn-compatible model performance with metrics and plots.

ds-utils-unsupervised

Visualize clustering results to evaluate cluster cardinality and magnitude.

ds-utils-strings

Encode comma-separated tags into boolean DataFrame columns and extract significant terms.

ds-utils-preprocess

Visualize feature distributions, correlations, and interactions from data science preprocessing workflows.

ds-utils-xai

Plot feature importances and render DOT-based decision trees for sklearn-compatible models.

ds-utils-transformers

Encode multi-label sets and text into sklearn pipeline features.

Community

Intermediate

Frequently Asked Questions

FAQPage Schema

How to install DataScienceUtils?▼

Run `npx skills add idanmoradarthas/DataScienceUtils --all -g -y` in your terminal to install all skills in this suite globally.

How to plot a confusion matrix in Python?▼

Call plot_confusion_matrix with your true labels, predictions, and class labels to get a ready-made chart with accuracy, F1, and error rates.

How to find the optimal number of clusters?▼

Use plot_loss_vs_cluster_number to chart K-Means loss across a range of k values and pick the elbow point visually.

Does DataScienceUtils work with sklearn pipelines?▼

Yes. It includes sklearn-compatible transformers for multi-label binarization and sentence embeddings that support get_feature_names_out and pandas output.

Can I visualize feature importance for my model?▼

Yes. The plot_features_importance function renders a ranked bar chart for any tree-based model that exposes feature_importances_.