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:

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:

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:

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:

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:

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).