| Task |
Assigned to |
1. To get IRIS data set
http://archive.ics.uci.edu/ml/datasets/Iris |
Everybody |
2. To describe the data set
- Origin, attributes, classes
- "Scatter plot"
- 2D visualization (PCA or MDS)
|
- David Camilo Becerra Romero
- José Luis Morales
|
3. Decision trees:
- train a decision tree
- describe the obtained model
- evaluate its performance
|
- Rolando Beltran Arrieta
- Daniel Restrepo Montoya
|
4. Neural network:
- design an appropriate network to solve the problem
- train it
- evaluate its performance
|
- Alexander Ceron Correa
- Luis Alejandro Riveros Cruz
|
5. Naïve Bayes:
- train a Naive-Bayes classifier
- describe the obtained model
- evaluate its performance
|
- Jimmy Alexander Cifuentes Rodriguez
- Maria Eugenia Rojas Izaquita
|
6. Linear Regression:
- propose a linear model to solve the problem (1 class against the others, multi-class)
- evaluate its performance
|
- Emir Fredy Cortes Trujillo
- Edwin Andres Niño Velasquez
|
7. k-nearest neighbors
- train a k-nn classifier
- describe the obtained model
- evaluate its performance
|
- Miguel Dario Dussan Sarria
- Omar Guillermo Erazo
|
8. Support Vector Machine:
- train a SVM classifier
- describe the obtained model
- evaluate its performance
|
- Camilo Ernesto López Guarín
- Sandra Patricia Tocarruncho Tocarruncho
|
10. K-means:
- cluster the data using K-means
- describe the obtained clustering
- Visualize the results
|
- Carlos Alfonso Garzon Mape
- Wilson Eduardo Soto Forero
|
11. Hierarchical Clustering:- cluster the data using hierarchical clustering
- describe the obtained clustering
- Visualize the results
| - Jeison Dario Gutierrez Juya
- Javier Fernando Vargas Gonzalez
|