🧩 Part 8/8

✔ Summary of the Tutorial Series
✔ F1 Score: Definition and Interpretation
✔ F1 Score: Calculation and Optimization
✔ F1 Score: Applications and Limitations
✔ Future Work and Recommendations
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🧩 Part 7/8

✔ What is F1 Score and Why is it Important?
✔ What is Feature Selection and How Does it Help?
✔ Common Feature Selection Methods
✔ Filter Methods
✔ Wrapper Methods
✔ Embedded Methods
✔ Comparing Feature Selection Methods on a Sample Dataset
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🧩 Part 6/8

✔ What is F1 Score and Why is it Important?
✔ What is Hyperparameter Tuning and How Does it Work?
✔ Grid Search vs Random Search: Pros and Cons
✔ How to Implement Grid Search and Random Search in Python
✔ How to Evaluate and Compare the Results of Grid Search and Random Search
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🧩 Part 5/8

✔ What is F1 Score and Why is it Important?
✔ Sampling Techniques for Imbalanced Data
✔ Oversampling Methods
✔ Undersampling Methods
✔ How to Apply SMOTE and NearMiss in Python
✔ How to Evaluate F1 Score and Compare Different Models
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🧩 Part 4/8

✔ What is Class Imbalance and Why is it a Problem?
✔ What is F1 Score and Why is it a Good Metric for Imbalanced Data?
✔ How to Use Class Weights to Adjust the Loss Function
✔ How to Implement Class Weights in Sklearn
✔ How to Evaluate the Performance of the Model with F1 Score
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🧩 Part 3/8

✔ What is F1 Score and Why is it Important?
✔ How to Calculate F1 Score for Binary Classification
✔ How to Plot ROC Curve and Precision-Recall Curve
✔ How to Find the Optimal Threshold Using F1 Score
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🧩 Part 2/8

✔ What is F1 Score and Why is it Important?
✔ Precision and Recall
✔ Harmonic Mean and F1 Score
✔ How to Calculate F1 Score in Python using sklearn.metrics
✔ Binary Classification Example
✔ Multiclass Classification Example
✔ How to Interpret F1 Score and Improve Model Performance
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🧩 Part 1/8

✔ What is F1 score and why is it useful?
✔ How to calculate F1 score from precision and recall
✔ What are precision and recall and how to interpret them?
✔ How to compute F1 score from a confusion matrix?
✔ How to use F1 score to evaluate and compare classification models
✔ How to choose the best threshold for a binary classifier?
✔ How to handle imbalanced classes and multi-class problems?
✔ How to improve F1 score and model performance
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