Evaluation Metrics CS229 April 23, 2021 Topics ● Why are metrics important? ● Binary classifiers ○ Rank view, Thresholding ● Metrics ○ Confusion Matrix ○ Point metrics: Accuracy, Precision, Recall / Sensitivity, Specificity, F-score ○ Summary metrics: AU-ROC, AU-PRC, Log-loss. ● Choosing Metrics ● Class Imbalance ○ Failure scenarios for each metric ● Multi-class Why […]
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Logistic Regression and MaxEnt Wei Wang @ CSE, UNSW April 9, 2020 1/23 Wei Wang @ CSE, UNSW Logistic Regression and MaxEnt Generative vs. Discriminative Learning Generative models: Pr[y | x] = Pr[x | y]Pr[y] Pr[x] ∝ Pr[x | y]Pr[y] = Pr[x, y] The key is to model the generative probability: Pr[x | y]. Example:
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COMP9318 Review Yifang Sun @ UNSW April 22, 2021 Data Warehousing and OLAP Understand the four characteristics of DW (DW vs. Data Mart) Differences between OLTP and OLAP Multidimensional data model; data cube; fact, dimension, measure, hierarchies cuboid, cube lattice three types of schemas four typical OLAP operations
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COMP9318: Data Warehousing and Data Mining — L7: Classification and Prediction — Data Mining: Concepts and Techniques 1 n Problem definition and preliminaries Data Mining: Concepts and Techniques 2 ML Map Data Mining: Concepts and Techniques 3 Classification vs. Prediction n Classification: n predicts categorical class labels (discrete or nominal) n classifies data (constructs a
THE UNIVERSITY OF NEW SOUTH WALES School of Computer Science and Engineering Final Examination– Term1, 2021 30th April, 2021 COMP9321 Data Service Engineering Total Exam Mark: 40 Total Number of Questions: 20 + 10 Exam Duration: 2 Hours +15 minutes (reading and submitting) **** IMPORTANT NOTICE**** There are Two parts in this exam paper: Part
Course code & title : Session : Time allowed : IS6400 Business Data Analytics Semester B 2020/21 Two hours CITY UNIVERSITY OF HONG KONG This paper has 8 pages (including this cover page). 1. This exam consists of FIVE questions. 2. Please answer ALL FIVE questions. 3. Please provide all your answers on a new
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Machine Learning and Data Mining in Business Lecture 2: Machine Learning Fundamentals Discipline of Business Analytics Copyright By PowCoder代写 加微信 powcoder Lecture 2: Machine Learning Fundamentals Learning objectives • Predictions and decisions. • Building blocks of learning algorithms. • Overfitting and the bias-variance trade-off. Basics of supervised learning Supervised learning • In supervised learning, we
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Tree Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Tree Learning Term 2, 2021 1 / 67 Acknowledgements Material derived from slides for the book “Machine Learning” by T. Mitchell McGraw-Hill (1997) http://www-2.cs.cmu.edu/~tom/mlbook.html Material derived from slides by Andrew W. Moore http:www.cs.cmu.edu/~awm/tutorials Material derived from slides by Eibe Frank
Ensemble Learning COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Ensemble Learning Term 2, 2021 1 / 51 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
Kernel Methods COMP9417 Machine Learning and Data Mining Term 2, 2021 COMP9417 ML & DM Kernel Methods Term 2, 2021 1 / 47 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by T. Hastie, R. Tibshirani & J. Friedman. Springer (2009) http://statweb.stanford.edu/~tibs/ElemStatLearn/ Material derived from slides for the book
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