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--- |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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pipeline_tag: tabular-classification |
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tags: |
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- classification |
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- crop-health |
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--- |
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# Model Card for Infinitode/PSPM-OPEN-ARC |
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Repository: https://github.com/Infinitode/OPEN-ARC/ |
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## Model Description |
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OPEN-ARC-PSP is a straightforward XGBClassifier model developed as part of Infinitode's OPEN-ARC initiative. It was designed to potentially identify plants experiencing high stress caused by external factors. |
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**Architecture**: |
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- **XGBClassifier**: `n_estimators=100`, `learning_rate=0.1`, `max_depth=6`, `subsample=0.8`, `colsample_bytree=0.8`, `random_state=42`. |
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- **Framework**: XGBoost |
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- **Training Setup**: Trained with the default training params. |
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## Uses |
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- Identifying crops experiencing significant stress. |
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- Improving crop production by mitigating major stressors affecting plants. |
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- Performing experimental studies on plant behavior and yield outcomes influenced by stress levels. |
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## Limitations |
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- May generate implausible or inappropriate results when influenced by extreme outlier values. |
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- Could provide inaccurate plant stress levels; caution is advised when relying on these outputs. |
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## Training Data |
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- Dataset: Plant-Health-Data dataset from Kaggle. |
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- Source URL: https://www.kaggle.com/datasets/ziya07/plant-health-data |
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- Content: Soil characteristics, moisture levels, and various agricultural metrics, combined with the anticipated stress level of the plant. |
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- Size: 1200 entries of plant stress levels. |
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- Preprocessing: Dropped unnecessary features like the `Timestamp` and `Plant_ID`. Stress levels were manually mapped to three distinct numerical values. |
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## Training Procedure |
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- Metrics: accuracy, precision, recall, F1 |
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- Train/Testing Split: 80% train, 20% testing. |
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## Evaluation Results |
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| Metric | Value | |
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| ------ | ----- | |
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| Testing Accuracy | 99.1% | |
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| Testing Weighted Average Precision | 99% | |
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| Testing Weighted Average Recall | 99% | |
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| Testing Weighted Average F1 | 99% | |
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## How to Use |
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```python |
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import random |
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def test_random_samples(model, X_test, y_test, n_samples=5): |
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""" |
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Selects random samples from the test set, makes predictions, and compares with actual values. |
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Parameters: |
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- model: Trained XGBoost classifier. |
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- X_test: Feature set for testing. |
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- y_test: True labels for testing. |
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- n_samples: Number of random samples to test. |
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Returns: |
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None |
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""" |
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# Convert X_test and y_test to DataFrame for easier indexing |
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X_test_df = X_test.reset_index(drop=True) |
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y_test_df = y_test.reset_index(drop=True) |
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# Pick random indices |
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random_indices = random.sample(range(len(X_test)), n_samples) |
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print("Testing on Random Samples:") |
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for idx in random_indices: |
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sample = X_test_df.iloc[idx] |
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true_label = y_test_df.iloc[idx] |
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# Predict using the model |
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prediction = model.predict(sample.values.reshape(1, -1)) |
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# Reverse the health mapping |
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reverse_health_mapping = {v: k for k, v in health_mapping.items()} |
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# Map true and predicted labels |
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true_label_description = reverse_health_mapping[true_label] |
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predicted_label_description = reverse_health_mapping[prediction[0]] |
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# Output results |
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print(f"Sample Index: {idx}") |
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print(f"Features: {sample.values}") |
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print(f"True Label: {true_label}, Predicted Label: {prediction[0]}") |
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print(f"True Label (Description): {true_label_description}, Predicted Label (Description): {predicted_label_description}") |
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print("-" * 40) |
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# Example usage |
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test_random_samples(xgb, X_test, y_test) |
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``` |
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## Contact |
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For questions or issues, open a GitHub issue or reach out at https://infinitode.netlify.app/forms/contact. |
