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{
"name": "49_Explainable_AI_LIME_Titanic_ML",
"query": "Hi there! I'm looking to create a project that explains model predictions using LIME, specifically with the Titanic survival prediction dataset. First, load the dataset in `src/data_loader.py`.Then, train a Random Forest classifier and save it under `models/saved_models/`? Finally, use LIME to explain the Random Forest classifier predictions and implement it in `src/visualize.py`. Generate a report including the explanations and save it as `results/model_explanation.md`. The report should be built with either Dash or Bokeh, implemented in `src/report.py`, so users can explore how different features affect the model's predictions. The explanation should be clear and easy to understand for non-tech folks. Additionally, save a well-labeled intuitive feature importance plot in `results/figures/feature_importance.png`. Thanks!",
"tags": [
"Classification"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Titanic\" survival prediction dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "A \"Random Forest classifier\" is trained for survival prediction.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0,
1
],
"criteria": "\"LIME\" is used for model prediction explanation and implemented in `src/visualize.py`.",
"category": "Human Computer Interaction",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
1,
2
],
"criteria": "A model prediction explanation report is generated and saved as `results/model_explanation.md`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2
],
"criteria": "A feature importance plot is saved as `results/figures/feature_importance.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2,
4
],
"criteria": "An interactive report showcasing the impact of different features on predictions is created using \"Dash\" or \"Bokeh\" and implemented in `src/report.py`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
1
],
"criteria": "The trained model is saved under `models/saved_models/`.",
"category": "Save Trained Model",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The explanation report should be written in a clear and accessible style, making it understandable even for those without a deep technical background.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The feature importance plot should be visually intuitive, with clear labels and descriptions.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |