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--- |
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datasets: |
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- dummy-data |
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library_name: scikit-learn |
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license: apache-2.0 |
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metrics: |
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- r2_score |
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model_name: Linear Regression Model V2 |
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tags: |
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- linear-regression |
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- example |
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- scikit-learn |
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--- |
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# Linear Regression Model V2 |
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This is a simple linear regression model trained on a dummy dataset. |
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## Model Description |
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This model predicts a `target` variable based on two features, `feature1` and `feature2`. It's a basic example to demonstrate model saving and uploading to Hugging Face Hub. |
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## Training Data |
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The model was trained on a small, synthetic dataset: |
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feature1: [1, 2, 3, 4, 5] |
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feature2: [5, 4, 3, 2, 1] |
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target: [2, 4, 6, 8, 10] |
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## Usage |
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To use this model, you can load it using `joblib` and make predictions: |
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import joblib |
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from huggingface_hub import hf_hub_download |
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# Download the model file |
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model_path = hf_hub_download(repo_id="Ashpgsem/rdmai", filename="linear_regression_modelV2.joblib") |
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# Load the model |
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model = joblib.load(model_path) |
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# Make a prediction |
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import pandas as pd |
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new_data = pd.DataFrame([{'feature1': 6, 'feature2': 0}]) |
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prediction = model.predict(new_data) |
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print(f"Prediction: {prediction}") |
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## Evaluation |
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Since this is a dummy model, formal evaluation metrics are not extensively provided. The model perfectly fits the provided dummy data. |
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## Limitations |
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This model is for demonstration purposes only and should not be used for real-world applications without proper training on relevant data and thorough evaluation. |
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