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Assignment 1-Naïve Bayes and k-Nearest Neighbor classifiers.
The main goal of this assignment is that students
obtain hands-on experience in using tools for Naïve Bayes and k-Nearest Neighbor classifiers.
For Naïve Bayes and k-NN you will use Weka or Scikit.
You will work with the 4 datasets posted on Resources (clusterincluster, halfkernel,
twogaussians and twosprials). Each dataset contains two-dimensional samples that belong to one
of the two classes: 1 or 2. The class labels (1 or 2) for each sample are located in the last column.
The first and second columns contain the coordinates of the 2D points that represent the samples.
1. Using Weka or Scikit, run the following classifiers on all 4 datasets:
a) k-NN with the Euclidean distance function, where k = 1. When do you think we need to
consider a tie-resolution scheme for k-NN? Give clear reasons for it.
b) The Naïve Bayes classifier that uses normal distributions, with default parameters.
c) Specify how you have used the tool for running these classifiers. Also, provide two
sample screenshots (before/after classifying) for each classifier on one dataset
2. Evaluate the two classification methods using 10-fold cross validation, obtaining for each
classifier the five measures of efficiency studied in class: PPV, NPV, specificity,
sensitivity, accuracy, where class 1 corresponds to “positive” and class 2 to “negative”.
Explain (in 5-6 lines) how 10-fold cross validation works.
3. For each classifier show the results obtained for the 4 datasets.
4. For the k-NN find the best value of k, and compare it with those of 1-NN and Naïve Bayes
classifiers. What is the best value of k for each dataset? Why?
5. Plot the samples of the 4 datasets (in 4 separate plots) as points in the 2D space, using a
different color and point shape for each class.
6. Briefly discuss the following for each dataset individually:
a) Explain the behavior of each classifier. Why do you think it is good/poor?
c) In your opinion, which classifier is the best for that particular dataset, and why?
Submit the following:
1) A report in PDF showing the items as required (this may not be needed):
a) Explain how you ran the classifiers using Weka or Scikit.
b) Classification measures (accuracy and others) for each classifier.
c) Discussions and comparisons as required.
2) Provide the source code used to run the classifiers or if used the Weka GUI, two
screenshots that show how you ran the classifiers in Weka.
obtain hands-on experience in using tools for Naïve Bayes and k-Nearest Neighbor classifiers.
For Naïve Bayes and k-NN you will use Weka or Scikit.
You will work with the 4 datasets posted on Resources (clusterincluster, halfkernel,
twogaussians and twosprials). Each dataset contains two-dimensional samples that belong to one
of the two classes: 1 or 2. The class labels (1 or 2) for each sample are located in the last column.
The first and second columns contain the coordinates of the 2D points that represent the samples.
1. Using Weka or Scikit, run the following classifiers on all 4 datasets:
a) k-NN with the Euclidean distance function, where k = 1. When do you think we need to
consider a tie-resolution scheme for k-NN? Give clear reasons for it.
b) The Naïve Bayes classifier that uses normal distributions, with default parameters.
c) Specify how you have used the tool for running these classifiers. Also, provide two
sample screenshots (before/after classifying) for each classifier on one dataset
2. Evaluate the two classification methods using 10-fold cross validation, obtaining for each
classifier the five measures of efficiency studied in class: PPV, NPV, specificity,
sensitivity, accuracy, where class 1 corresponds to “positive” and class 2 to “negative”.
Explain (in 5-6 lines) how 10-fold cross validation works.
3. For each classifier show the results obtained for the 4 datasets.
4. For the k-NN find the best value of k, and compare it with those of 1-NN and Naïve Bayes
classifiers. What is the best value of k for each dataset? Why?
5. Plot the samples of the 4 datasets (in 4 separate plots) as points in the 2D space, using a
different color and point shape for each class.
6. Briefly discuss the following for each dataset individually:
a) Explain the behavior of each classifier. Why do you think it is good/poor?
c) In your opinion, which classifier is the best for that particular dataset, and why?
Submit the following:
1) A report in PDF showing the items as required (this may not be needed):
a) Explain how you ran the classifiers using Weka or Scikit.
b) Classification measures (accuracy and others) for each classifier.
c) Discussions and comparisons as required.
2) Provide the source code used to run the classifiers or if used the Weka GUI, two
screenshots that show how you ran the classifiers in Weka.
1 file (1.7MB)
