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ML projects

Machine Learning Model Evaluation

This repository contains the results of experimenting with various machine learning models for a specific task. The goal was to identify the most suitable algorithm for the given problem. The following algorithms were tried, and their corresponding F1 scores were obtained:

1. Decision Tree Model

   • F1 Score: 0.32

2. KNeighborsClassifier

   • F1 Score: 0.35

3. Logistic Regression Model

   • F1 Score: 0.45

4. Random Forest Classifier

   • F1 Score: 0.53

5. XGBoost Classifier

   • F1 Score: 0.55

6. LGBM Classifier

   • F1 Score: 0.56

7. Naive Bayes GaussianNB

   • F1 Score: 0.56

Conclusion

After thorough experimentation, it was observed that the highest F1 score was achieved with the LGBM Classifier and the Naive Bayes GaussianNB algorithms, both scoring 0.56. Further details about the models, their hyperparameters, and the data used can be found in the respective folders.

Feel free to explore each model's directory for more in-depth information.

This README template provides a brief overview of the models, their corresponding F1 scores, and encourages users to explore each model's directory for more detailed information. Adjust the content as needed based on your specific experiment and requirements.