Access Millions of academic & study documents

Business Analytics And Machine Learning

Content type
User Generated
Subject
Statistics
Type
Homework
Showing Page:
1/11
Running Header: BUSINESS ANALYTICS AND MACHINE LEARNING 1
Business Analytics and Machine Learning
Institutional Affiliation
Date

Sign up to view the full document!

lock_open Sign Up
Showing Page:
2/11
BUSINESS ANALYTICS AND MACHINE LEARNING 2
Question 2: Machine Learning
a) Which of unsupervised or supervised machine learning is best suited to assessing
causation? Explain your choice.
Unsupervised learning technique is best suited for assessing causation. Unsupervised learning
techniques rely on latent variables to assess for causation. With unsupervised learning, it is
possible to learn larger and more complex models than with supervised learning. This is because
in supervised learning one is trying to find the connection between two sets of observations. The
causal structure of supervised learning technique assumes that you have inputs at the start of the
model and outputs at the end. The difficulty of the learning task increases exponentially in the
number of steps between the two sets and that is why supervised learning cannot, in practice,
learn models with deep hierarchies.
b) Your analytics team presents you with two sets of results that have improved the
organization’s ability to predict customer defections. The first method uses deep learning
and has a precision of 85%. The second method uses decision trees and has a precision of
70%. The previous approach had a precision of 40%.
i) Make a case for using the results of the deep learning method.
Deep learning methods perform best under situations where the data is unstructured (audio,
images, text, video). Given such a data set, I would consider using deep learning method to be
able to obtain better results.
ii) Make a case for using the decision tree method.
Decision trees are part of the random forests ensemble methods. Decision trees work best in
situations of binary classifications. Random forests are good in classification and prediction n

Sign up to view the full document!

lock_open Sign Up
Showing Page:
3/11

Sign up to view the full document!

lock_open Sign Up
End of Preview - Want to read all 11 pages?
Access Now
Unformatted Attachment Preview
Running Header: BUSINESS ANALYTICS AND MACHINE LEARNING Business Analytics and Machine Learning Institutional Affiliation Date 1 BUSINESS ANALYTICS AND MACHINE LEARNING 2 Question 2: Machine Learning a) Which of unsupervised or supervised machine learning is best suited to assessing causation? Explain your choice. Unsupervised learning technique is best suited for assessing causation. Unsupervised learning techniques rely on latent variables to assess for causation. With unsupervised learning, it is possible to learn larger and more complex models than with supervised learning. This is because in supervised learning one is trying to find the connection between two sets of observations. The causal structure of supervised learning technique assumes that you have inputs at the start of the model and outputs at the end. The difficulty of the learning task increases exponentially in the number of steps between the two sets and that is why supervised learning cannot, in practice, learn models with deep hierarchies. b) Your analytics team presents you with two sets of results that have improved the organization’s ability to predict customer defections. The first method uses deep learning and has a precision of 85%. The second method uses decision trees and has a precision of 70%. The previous approach had a precision of 40%. i) Make a case for using the results of the deep learning method. Deep learning methods perform best under situations where the data is unstructured (au ...
Purchase document to see full attachment
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.
Studypool
4.7
Indeed
4.5
Sitejabber
4.4