atdokmeci/integer_detection_model

Digit Recognition

Intended Use

This model is designed to classify handwritten digits (0-9) based on pixel values from the MNIST-like dataset. It is intended for educational purposes and to demonstrate the use of Random Forest for multi-class classification.

Training Data

• Dataset: The model was trained on a dataset with 42,000 samples, where each sample is a 28x28 grayscale image flattened into a vector of 784 pixel values.
• Labels: The dataset contains 10 classes (digits 0-9).
• Train-Test Split:
  • Training set: 33,600 samples (80%)
  • Validation set: 8,400 samples (20%)

Evaluation Metrics

• Accuracy: The model achieved an accuracy of approximately accuracy_score(y_val, y_pred) on the validation set.
• Classification Report: Includes precision, recall, and F1-score for each class.
• Confusion Matrix: Visualized to show the distribution of predictions across classes.

Limitations

• The model may not generalize well to digits written in styles significantly different from the training data.
• It is not optimized for real-time or large-scale applications.

Ethical Considerations

• Ensure the dataset used does not contain any biases that could affect the fairness of the model.
• The model should not be used in critical applications without further validation and testing.

How to Use

1. Load the model using joblib.load('digit_rf_model.joblib').
2. Preprocess the input data to match the format of the training data (28x28 images flattened into 784-pixel vectors).
3. Use the predict method to classify new samples.