# Rice Classification Model ## Overview This repository contains an XGBoost-based model trained to classify rice grains using the `mltrev23/Rice-classification` dataset. The model is designed to predict the type of rice grain based on various geometric and morphological features. XGBoost (eXtreme Gradient Boosting) is a powerful, efficient, and scalable machine learning algorithm that excels at handling structured data. ## Model Details ### Algorithm - **XGBoost**: A gradient boosting framework that uses tree-based models. XGBoost is known for its performance and speed, making it a popular choice for structured/tabular data classification tasks. ### Training Data - **Dataset**: The model is trained on the `mltrev23/Rice-classification` dataset. - **Features**: The dataset includes the following features: `Area`, `MajorAxisLength`, `MinorAxisLength`, `Eccentricity`, `ConvexArea`, `EquivDiameter`, `Extent`, `Perimeter`, `Roundness`, and `AspectRation`. - **Target**: The target variable is `Class`, a binary label indicating the type of rice grain. ### Model Performance - **Accuracy**: [Insert accuracy metric] - **Precision**: [Insert precision metric] - **Recall**: [Insert recall metric] - **F1-Score**: [Insert F1-score] (Replace the placeholders with actual values after evaluating the model on your test data.) ## Requirements To run the model, you'll need the following Python libraries: ```bash pip install xgboost pip install pandas pip install numpy pip install scikit-learn ``` ## Usage ### Loading the Model You can load the trained model using the following code snippet: ```python import xgboost as xgb # Load the trained model model = xgb.Booster() model.load_model('rice_classification_xgboost.model') ``` ### Making Predictions To make predictions with the model, use the following code: ```python import pandas as pd # Example input data (replace with your actual data) data = pd.DataFrame({ 'Area': [4537, 2872], 'MajorAxisLength': [92.23, 74.69], 'MinorAxisLength': [64.01, 51.40], 'Eccentricity': [0.72, 0.73], 'ConvexArea': [4677, 3015], 'EquivDiameter': [76.00, 60.47], 'Extent': [0.66, 0.71], 'Perimeter': [273.08, 208.32], 'Roundness': [0.76, 0.83], 'AspectRation': [1.44, 1.45] }) # Convert DataFrame to DMatrix for XGBoost dtest = xgb.DMatrix(data) # Predict class predictions = model.predict(dtest) ``` ### Evaluation You can evaluate the model's performance on a test dataset using standard metrics like accuracy, precision, recall, and F1-score: ```python from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score # Assuming you have ground truth labels and predictions y_true = [1, 0] # Replace with your actual labels y_pred = predictions.round() # XGBoost predictions may need to be rounded print("Accuracy:", accuracy_score(y_true, y_pred)) print("Precision:", precision_score(y_true, y_pred)) print("Recall:", recall_score(y_true, y_pred)) print("F1 Score:", f1_score(y_true, y_pred)) ``` ## Model Interpretability For understanding feature importance in the XGBoost model: ```python import matplotlib.pyplot as plt # Plot feature importance xgb.plot_importance(model) plt.show() ``` ## References If you use this model in your research, please cite the dataset and the following reference for XGBoost: - **Dataset**: `mltrev23/Rice-classification` - **XGBoost**: Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).