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The project focuses on evaluating and comparing various machine learning models to classify a dataset. The goal was to implement and assess baseline models, fine-tune advanced architectures, and develop an ensemble model to improve predictive performance. Methodology
Data preprocessing involved loading the dataset, normalizing features, and splitting the data into training and test sets. Augmentation techniques were applied to the training data to enhance the model's generalization capability.
1 Baseline Model: A simple neural network with a few layers.
2 ResNet50: A deep residual network known for its high accuracy, leveraging skip connections to improve gradient flow.
3 EfficientNet: A model balancing accuracy and computational efficiency by scaling network dimensions.
Each model was trained using the preprocessed dataset. Hyperparameters such as learning rate, batch size, and the number of epochs were tuned to optimize performance. The training process included validation steps to monitor overfitting and adjust model parameters accordingly.
Model Performance Metrics Performance metrics such as accuracy, precision, recall, and F1-score were used to evaluate each model. The results are as follows: • Baseline Model: Accuracy: 0.75, Precision: 0.74, Recall: 0.76, F1-score: 0.75 • ResNet50: Accuracy: 0.85, Precision: 0.84, Recall: 0.85, F1-score: 0.85 • EfficientNet: Accuracy: 0.88, Precision: 0.87, Recall: 0.88, F1-score: 0.88
A detailed comparison highlighted the strengths and weaknesses of each model. The ResNet50 showed higher accuracy but required more computational resources. EfficientNet provided a good balance between accuracy and efficiency, making it suitable for resource-constrained environments. Ensemble Model An ensemble model was created by averaging the predictions of the baseline, ResNet50, and EfficientNet models. The ensemble model showed an improved F1 score of 0.89, demonstrating the benefits of combining multiple models.
The project successfully implemented and compared different machine learning models for classification tasks. The ensemble approach provided the best performance, highlighting the effectiveness of model combinations. Future work could explore additional models and ensemble techniques to further enhance predictive accuracy.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
This project is licensed under the MIT License.