instances/49_Explainable_AI_LIME_Titanic_ML.json

{
    "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
}