In part 4 of Fast Track Machine Learning (Machine Learning Overview) series, Dr. Dakshinamurthy Kolluru explains about the three main aspects that are to be given importance while defining the architecture in Machine Learning. He explains about the difference between training, testing data and why is it important to keep testing data in a given data set.

1. Inspire…Educate…Transform.
Part 4 (Machine Learning
Overview) - Solution
Architecture
Dr. K. V Dakshinamurthy
President, INSOFE
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3. SOLUTION ARCHITECTURE
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4. Definition
• Goal
• Process
• Assumptions
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5. Example for the bank case
• From an experimental sample of
customers who accepted/rejected
loans, we will establish relations
between demographic and
transactional data and the decision on
loan.
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6. Structure of the problem
• Classification/Regression/Optimization
or a combination
• In the bank’s case it is a binary (yes or
no classification)
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7. Error can be asymmetric
• Predicting the takers as non-takers or
• Predicting non-takers as takers?
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8. Metrics
• A variety of metrics (precision,
sensitivity, specificity, F1 statistic,
AUC etc. have been developed)
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9. If the pattern is not there in the unseen
• Split the data into 3 bins: Training, testing
and evaluation sets
– Build models on training
– Tweak them using testing
– Declare the final accuracy based on
evaluation set
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10. YouTube link
• For a detailed video on this topic visit
the following link:
http://www.youtube.com/watch?v=yvA
3Ep6DK_U
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This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that the organization
subscribes to those findings.
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