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Remove feature selection cell from TabPFN_Demo_Local notebook #978

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Remove feature selection cell from TabPFN_Demo_Local notebook #978

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2c1586a
Remove feature selection cell from TabPFN_Demo_Local notebook
adrian-prior May 22, 2026
f02bb06
Add changelog entry
adrian-prior May 22, 2026
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1 change: 1 addition & 0 deletions changelog/978.changed.md
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Original file line number Diff line number Diff line change
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Remove the feature selection cell from the TabPFN_Demo_Local example notebook.
50 changes: 0 additions & 50 deletions examples/notebooks/TabPFN_Demo_Local.ipynb
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Expand Up @@ -3177,56 +3177,6 @@
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PkphS4vSFNvg"
},
"source": [
"### Feature selection\n",
"\n",
"Feature selection is the process of selecting a subset of relevant features for use in model construction. It's useful for reducing model complexity, improving performance by removing noise, and decreasing training time. Here, we'll use Sequential Forward Selection (SFS), which starts with no features and iteratively adds the feature that most improves the model's performance.\n",
"\n",
"The goal is to see if we can create a simpler, faster model with fewer features without a significant drop in accuracy.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GPJtNDLnFPPW"
},
"outputs": [],
"source": [
"from tabpfn_extensions import interpretability\n",
"\n",
"# Load data\n",
"data = load_breast_cancer()\n",
"X, y = data.data, data.target\n",
"feature_names = data.feature_names\n",
"\n",
"# Initialize model\n",
"clf = TabPFNClassifier(n_estimators=1)\n",
"\n",
"# Feature selection\n",
"sfs = interpretability.feature_selection.feature_selection(\n",
" estimator=clf,\n",
" X=X,\n",
" y=y,\n",
" n_features_to_select=4,\n",
" feature_names=feature_names,\n",
" cv=3,\n",
")\n",
"\n",
"# Print selected features\n",
"selected_features = [\n",
" feature_names[i] for i in range(len(feature_names)) if sfs.get_support()[i]\n",
"]\n",
"print(\"\\nSelected features:\")\n",
"for feature in selected_features:\n",
" print(f\"- {feature}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
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