The document discusses multiclass classification concepts, including confusion matrices, performance metrics, and ROC curves. It differentiates between binary and multiclass scenarios, explaining the one-vs-rest (OVR) and one-vs-one (OVO) approaches for constructing ROC curves. Additionally, it covers how to calculate accuracy, precision, recall, and F1-score from confusion matrices.

1. Multiclass Classification
-Ishwarya C
 Binary VS Multiclass
 Confusion Matrix
 Performance Metrics
 ROC curve
 OVR or OVO Approaches
 Hands-on Code

2. Binary vs. Multiclass Classification

3. Confusion Matrix
 A Confusion Matrix is a table used to evaluate the performance of a classification
model. It compares the predicted labels with the actual labels. It helps to see if the
model is correctly classifying data or making errors.
 The matrix consists of four key elements:
1. True Positives (TP): Correctly predicted positive cases.
2. True Negatives (TN): Correctly predicted negative cases.
3. False Positives (FP): Incorrectly predicted positive cases (Type I error).
4. False Negatives (FN): Incorrectly predicted negative cases (Type II error).
 From these, various metrics like Accuracy, Precision, Recall, and F1-Score can be
calculated.

14. Multi-Class ROC Curve
•A Receiver Operating Characteristic (ROC) curve is a graphical representation of a classifier's
performance across different thresholds.
• ROC analysis is inherently binary.
• For multi-class problems, strategies include:
• One-vs-Rest (OvR): Compute ROC for each class versus all other classes.
• One-vs-One (OvO): Compute ROC for each pair of classes.
• Aggregate metrics like macro-average or micro-average are used to summarize performance.

15. Key Differences in Binary and
Multiclass ROC
Feature Binary ROC Multiclass ROC
Number of
Curves
Single ROC
curve
One ROC curve
per class (OvR)
Thresholds
Applied to
probabilities of
one class
Applied to
probabilities for
each class
Performance
Metric
AUC (Area
Under Curve)
AUC for each
class
(Macro/Micro
averages)
Interpretation Simpler
More complex
(requires per-
class analysis)
Key Differences in Binary and Multiclass ROC

16. ONE-VS-ALL (ONE-VS-REST)

17. ONE-VS-ALL (ONE-VS-REST)

18. ONE-VS-One(OVO)

19. ONE-VS-One(OVO)

20. key differences between One-vs-Rest
(OVR) and One-vs-One (OVO)

21. Approaches to Construct ROC for
Multiclass:
 One-vs-Rest (OvR):
For each class, treat it as the positive class and all other classes as the negative class.
•Steps:
•Calculate TPR and FPR for one class vs the rest.
•Repeat for all classes to generate one ROC curve per class.
•Metrics:
•Compute AUC for each class individually.
•Aggregate performance:
•Macro-Averaged AUC: Average AUC of all classes.
•Micro-Averaged AUC: Compute TPR and FPR globally across all classes.

22. THANK YOU