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license: apache-2.0 |
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Model Card: TERPredictor V1 |
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📌 Model Name |
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TERPredictor V1 – A linear regression model for predicting the Total Expense Ratio (TER) of mutual fund Regular Plans. |
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📖 Overview |
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TERPredictor V1 is a regression model trained to estimate the 'Regular Plan - Total TER (%)' of mutual funds based on various financial features. It uses a simple linear regression approach and achieves near-perfect performance on the test set. Due to the unusually high accuracy, this model is best suited for exploratory analysis and feature relationship interpretation, rather than generalization to unseen data. |
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📊 Intended Uses |
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Expense Ratio Estimation: Estimate TER for new or hypothetical mutual fund structures. |
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Outlier Detection: Identify funds with unusually high or low TERs. |
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Feature Impact Analysis: Understand which components most influence TER. |
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🧠 Model Architecture |
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Attribute Value |
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Model Type Linear Regression |
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Framework scikit-learn |
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Input Features 10 float64 columns |
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Target Variable Regular Plan - Total TER (%) |
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Identifier Dropped Scheme Name (object) |
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📚 Training Details |
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Dataset Size: 1,622 samples |
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Train/Test Split: 1297 / 325 |
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Missing Values: None |
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Preprocessing: |
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Dropped identifier column (Scheme Name) |
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No normalization required due to linear model simplicity |
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📈 Evaluation Metrics |
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Metric Value |
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Mean Squared Error (MSE) 0.000001 |
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R-squared (R²) 0.999999 |
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⚠️ Note: These metrics suggest potential data leakage or a deterministic relationship between features and target. Use with caution. |
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🚀 How to Use |
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python |
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from terpredictor import TERModel |
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model = TERModel.load_pretrained("your-huggingface-username/terpredictor-v1") |
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input_data = { |
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"feature_1": 0.12, |
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"feature_2": 0.03, |
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... |
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} |
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predicted_ter = model.predict(input_data) |
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⚠️ Limitations |
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Potential Data Leakage: Extremely high R² may indicate the target is directly derived from input features. |
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Limited Generalization: Not recommended for predicting TER on unseen or structurally different funds. |
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No Feature Engineering: Model assumes raw features are sufficient. |
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📄 License |
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MIT License |
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👤 Author |
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Created by [Your Name or Organization] |
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📚 Recommendations for Open-Sourcing |
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Include full training code and preprocessing steps |
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Provide detailed explanation of evaluation metrics |
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Add cautionary notes about performance anomalies |
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Consider publishing a cleaned or anonymized version of the dataset |
