This document provides an outline for a WEKA tutorial that discusses various data preprocessing techniques including creating datasets, generating training/validation/test sets, discretization, numeric transformation, and handling outliers. It also gives examples of decision tree and dataset exploration in WEKA.

1. WEKA TUTORIAL
Presenter
Sajib Sen
1

2. Outline:
• Data Preprocessing
• Data Dimensionality
• Classification
• Model Evaluation

3. Data Preprocessing
1. Creating dataset
2. Creating Training, Validation and Test Sets
3. Generating Non-stratified Folds
4. Generating Stratified Folds
5. Discretization
6. Numeric Transform
7. Outliers and Extreme Values
4/30/18 3

4. Decision Tree Example
4/30/18 4

5. Dataset Example
• How to create a arff file
• How to open a file
• How to see number of attribute instances and attributes
• How to see the features and their information
Weather_data.arff
4/30/18 5

6. Creating Training, Validation and Test Sets
Training set: 60%
Cross validation: 20%
Test set: 20%
weather.arff
4/30/18 6

7. Generating Non-stratified Folds
When I am using k-fold cross validation, can I get each of the folds
from WEKA?
Stratified folds means every fold has every class of your dataset with
maintaining class ratio.
supermarket.arff
4/30/18 7

8. Generating Stratified Folds
When I am using k-fold cross validation, can I get each of the folds
from WEKA?
Stratified folds means every fold has every class of your dataset with
maintaining class ratio.
supermarket.arff
4/30/18 8

9. Discretization
Naïve bayes works well when values are discretized
Diabetes.arff
4/30/18 9

10. Numeric Transform
When your algorithm works well with integer but not real numbers.
Diabetes.arff
4/30/18 10

11. Outliers and Extreme Values
Is this possible to find out outliers and extreme values that are hidden in
dataset?
InterQuartileRange
Find outlier and extreme values.
Remove them.
nsl kdd dataset arff
4/30/18 11