CS代考程序代写 LECTURE 2 TERM 2:

LECTURE 2 TERM 2:
MSIN0097
Predictive Analytics Video 5: Classification
A P MOORE

CL ASSIFIC ATION
A. ClAssification B. Regression
C. Clustering D. Decomposition
Supervised
Unsuper vised

A. CLASSIFICATION CATEGORICAL VARIABLE

GRID (PIXEL) DATA

VARIATION IN DIGITS

PARTITIONING DATA

K-FOLD VALIDATION

CONFUSION MATRIX BINARY FORCED CHOICE
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A. CLASSIFICATION CATEGORICAL VARIABLE
Model 1
predicted
predicted
Model 2
actual
actual

CONFUSION MATRIX

SINGLE METRICS
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑅𝑒𝑐𝑎𝑙𝑙 =
𝐹1 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑃 𝑇𝑃
𝑇𝑃 + 𝐹𝑁 2
=2×𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛×𝑅𝑒𝑐𝑎𝑙𝑙= 𝑃𝑟𝑒𝑐𝑖𝑖𝑠𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
𝑇𝑃
𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃 2
1
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑅𝑒𝑐𝑎𝑙𝑙
+
1

SENSITIVITY, SPECIFICITY…
https://en.wikipedia.org/wiki/Confusion_matrix
Prevalence
Accuracy
Positive predictive value False discovery rate False omission rate Diagnostic odds ratio…

PRECISION / RECALL TRADE OFF
Decision function

DECISION THRESHOLD

PR CURVE

ROC CURVE & AUC
https://en.wikipedia.org/wiki/Confusion_matrix

AUC – MODEL SELECTION

BINARY CLASSIFICATION

MULTICL ASS

MULTICL ASS
— one-versus-the-rest (OvR) strategy (also called one-versus-all) — one-versus-one (OvO) strategy

CONFUSION MATRIX

CONFUSION IMAGE

CONFUSION ERRORS

ANALYZING ERRORS 3 VS 5
TP1 0FN 1
0
FP TN

FAILURE MODES – GENERALIZATION

CL ASSIFIC ATION
— Multilabel Classification
— Multioutput Classification

DENOISING

REVIEW
— Select good metrics for classification tasks
— How to pick the appropriate precision/recall trade-off
— How to compare classifiers
— Different classification systems for a variety of tasks
— What business problems can you think of that are classification tasks?
— Can you think of some business problems that are multilable and multioutput?

TEACHING TEAM
Dr Alastair Moore Senior Teaching Fellow
a.p.moore@ucl.ac.uk
@latticecut
Kamil Tylinski Teaching Assistant
kamil.tylinski.16@ucl.ac.uk
Jiangbo Shangguan Teaching Assistant
j.shangguan.17@ucl.ac.uk

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