Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+pretty_name: ML Model Failures Dataset
+size_categories:
+- n<1K
+task_categories:
+- tabular-classification
+tags:
+- machine-learning
+- debugging
+- model-failures
+- overfitting
+- underfitting
+- adversarial
+- bias
+- class-imbalance
+- gradient-problems
+- model-drift
+- out-of-distribution
+- mlops
+- education
+---
+
+# ML Model Failures Dataset
+
+## What Is This
+
+This dataset contains 900 annotated machine learning failure records 
+across 9 failure types. Every record documents what went wrong, why it 
+went wrong, and how to fix it.
+
+---
+
+## Load It In Python
+
+from datasets import load_dataset
+
+ds = load_dataset("YOUR_HF_USERNAME/ml-failures-dataset")
+df = ds['train'].to_pandas()
+
+print(df['failure_type'].value_counts())
+print(df['severity'].value_counts())
+
+---
+
+## The 9 Failure Types
+
+1. Overfitting — High severity
+2. Underfitting — High severity
+3. Adversarial Attack — Critical severity
+4. Bias — Critical severity
+5. Class Imbalance — High severity
+6. Gradient Exploding — High severity
+7. Gradient Vanishing — High severity
+8. Model Drift — High severity
+9. Out-of-Distribution — Critical severity
+
+---
+
+## All 14 Columns
+
+scenario_id — unique ID like OVF_0001
+failure_type — which failure category
+feature_values — input numbers as a list
+true_label — correct answer 0 or 1
+train_accuracy — accuracy on training data
+val_accuracy — accuracy on test data
+precision — weighted precision score
+recall — weighted recall score
+f1_score — weighted F1 score
+error_description — what went wrong
+root_cause — why it failed
+fix_strategy — how to fix it
+severity — Critical, High, or Medium
+extra_info — extra diagnostic info
+
+---
+
+## Average Metrics By Failure Type
+
+Overfitting          train 1.000  val 0.700  f1 0.700
+Underfitting         train 0.720  val 0.710  f1 0.710
+Adversarial Attack   train 0.880  val 0.650  f1 0.640
+Bias                 train 0.820  val 0.780  f1 0.760
+Class Imbalance      train 0.960  val 0.950  f1 0.910
+Gradient Exploding   train 0.500  val 0.500  f1 0.490
+Gradient Vanishing   train 0.500  val 0.500  f1 0.490
+Model Drift          train 0.880  val 0.600  f1 0.590
+Out-of-Distribution  train 0.950  val 0.480  f1 0.470
+
+---
+
+## How Data Was Created
+
+All data is synthetic and reproducible using scikit-learn.
+Run ML_Failures_Dataset.ipynb in Google Colab to regenerate everything.
+
+---
+
+## License
+
+MIT — free for research, education, and commercial use.
+
+---
+
+## Citation
+
+@dataset{ml_failures_dataset_2026,
+  title   = {ML Model Failures Dataset},
+  year    = {2026},
+  version = {1.0.0},
+  license = {MIT}
+}