Machine Learning & AI

Model performance metrics, dimensionality reduction, and clustering.

Specialized Visualizations

ROC-AUC Curve

scikit-learn

yellowbrick

matplotlib

A graph showing the performance of a classification model at all classification thresholds (True Positive Rate vs False Positive Rate).

Prompt

"Generate a proper example classification dataset and create an ROC curve using scikit-learn comparing the performance of 'Random Forest' vs 'Logistic Regression' models. Shade the area under the curve (AUC) and display the score."

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t-SNE / UMAP Projection

umap-learn

seaborn

A dimensionality reduction technique used to visualize high-dimensional data (like image embeddings) in 2D or 3D space.

Prompt

"Generate a proper example high-dimensional dataset and create a UMAP scatter plot using umap-learn. Color the points by their class labels to visualize how well the clusters separate."

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Confusion Matrix

seaborn

scikit-learn

A specific table layout that allows visualization of the performance of an algorithm (Actual vs Predicted classes).

Prompt

"Generate a proper example classification results dataset and create a confusion matrix heatmap using seaborn. Use a blue color scale and annotate each cell with the raw count and percentage."

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