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- Generate a classification (i.e., decision) tree model based on your training data set and test
- Generate a logistic regression model based on your training data set and test it on your test
- Conduct a K-nearest neighbors' analysis (k=7).
- Compare the performance of the three approaches (i.e., classification tree, logistic
- One KNIME workflow for each of the models/analyses above (you should submit three
- An MS Word document in which you include a snapshot of each of your KNIME workflows
Online Knime, Logistic regression, Data tutor - Bahawalpur, Pakistan - TeacherOn
Description
URGENTLY NEED SOMEONW WHO KNOWS HWO TO DO KNIMEThis is the desc:
For each analysis, partition the data set into training (70%) and test (30%) sets through "Stratified
sampling". Please make sure to select "Use random seed".
Use the following settings for the decision tree:
o Quality measure:
Gini index
o Pruning method:
MDL
o Check reduced error pruning
o Min number records per node: 2
(The answer to this question should be included in your managerial report)
What to submit:
your models/analyses, do NOT reset workflow(s) before export (i.e., this option should be
unchecked).
Please make sure that you include headers and/or section titles for different analyses
Level:
Beginner
Gender Preference:
None
Meeting options:
Available online - via skype etc.
At home - Student can meet at their place.