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
Load the model using joblib.load('digit_rf_model.joblib').
Preprocess the input data to match the format of the training data (28x28 images flattened into 784-pixel vectors).
