CS8080_IRT_UNIT - III T9 EVALUATION METRICS.pdf
•
0 likes•99 views
CS8080_IRT_UNIT - III T9 EVALUATION METRICS.pdf
Recommended
Recommended
More Related Content
Similar to CS8080_IRT_UNIT - III T9 EVALUATION METRICS.pdf
Similar to CS8080_IRT_UNIT - III T9 EVALUATION METRICS.pdf (20)
More from AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
More from AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING (13)
Recently uploaded
Recently uploaded (20)
CS8080_IRT_UNIT - III T9 EVALUATION METRICS.pdf
- 1. P1WU UNIT – III: CLASSIFICATION Topic 9: EVALUATION METRICS AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 2. UNIT III : TEXT CLASSIFICATION AND CLUSTERING 1.A Characterization of Text Classification 2. Unsupervised Algorithms: Clustering 3. Naïve Text Classification 4. Supervised Algorithms 5. Decision Tree 6. k-NN Classifier 7. SVM Classifier 8. Feature Selection or Dimensionality Reduction 9. Evaluation metrics 10. Accuracy and Error 11. Organizing the classes 12. Indexing and Searching 13. Inverted Indexes 14. Sequential Searching 15. Multi-dimensional Indexing AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 3. EVALUATION METRICS AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 4. EVALUATION METRICS • Evaluating the Accuracy of a Classifier • Basic Evaluation Measures for Classifier Performance. • In Bioinformatics and machine learning in general, • there is a large variation in the measures that are used to evaluate prediction systems. AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 5. A confusion matrix • For simplicity, the assumption is that each instance can only be assigned one of two classes: • Positive or • Negative (e.g. a patient's tumor may be malignant or benign). • Each instance (e.g. a patient) has a Known label, and a Predicted label. • Some method is used (e.g. cross-validation) to make predictions on each instance. Each instance then increments one cell in the confusion matrix. AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 6. EVALUATION METRICS: -A confusion matrix AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES Predicted Label Positive Negative Known Label Positive True Positive False Negative (TP) (FN) Negative False Positive True Negative (FP) (TN)
- 7. EVALUATION METRICS AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 8. CONTINGENCY TABLE AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 9. CONTINGENCY TABLE AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 10. Any Questions? AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES