Description
Given the IRIS dataset (consists of 150 samples, four input features, and three different
output classes), train and compute the performances of the following classifiers using 10-
fold cross-validations (10 FCV):
(1) [5 6 = 30 points] The classifiers are: (a) ETC (Extra Tree Classifier), (b) Bagging,
(c) DTC (Decision Tree Classifier), (d) LR (Logistic Regression), (e) SVC (support
vector classifier), and (f) kNN (k-Nearest Neighbor).
[Hints: you should be able to import those classifiers by calling the following lines
respectively:
from sklearn.tree import ExtraTreeClassifier,
from sklearn.ensemble import BaggingClassifier,
from sklearn.tree import DecisionTreeClassifier,
from sklearn.linear_model import LogisticRegression,
from sklearn.svm import SVC,
from sklearn.neighbors import KNeighborsClassifier.
]
(2) [7 6 = 42 points] Compute and show the following performance metrics for each of
the classifiers: (a) accuracy, (b) balanced accuracy, (c) Matthews Correlation Coefficient,
(d) Sensitivity, (e) Specificity, (f) F1-score, and (g) confusion matrix.
(3) [14 2 = 28 points] Build two different ensemble classifiers by Stacking [1-4] – each
of the classifiers will have a base layer and a meta-layer. Each base-layer will consist of
three base-classifiers, and each meta-layer will consist of one classifier – taken from the
classifiers listed in Question #1. Compute and show these two classifiers’ performance in
terms of the metrics listed in Question #2.
Stacking refers to a method to blend estimators where the base estimators are
individually fitted on some training data while a final or meta estimator is trained using
the stacked predictions of these base estimators. In your Stacking-based classifier
constructions, the base classifiers will provide three class-classification probabilities
[hints: use model_instance.predict_proba(X_test)], for each sample to the meta classifier.
Thus, the meta classifier will be trained using the original 4 input features plus 3
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probabilities from each of the three base classifiers, i.e., the meta classifier will have in
total (4 + 3 × 3) or 13 input features.
Submission via Moodle:
(1) A report in ~.pdf or ~.docx, containing each of the classifiers’ performance
metrics listed in Question #1 and Question #3 using Table(s).
(2) Your python code in jupyter notebook format/file.
(3) Additional datasets (if any) that you may have created and used to build the
classifiers based on Stacking – so that the grader can run and check your code
smoothly.
Compress all three items in a folder as ~.zip and submit via Moodle.
References:
[1] D. H. Wolpert, “Stacked Generalization,” Neural Networks, Elsevier., vol. 5, pp.
241-259, 1992.
[2] A. Mishra, P. Pokhrel, and M. T. Hoque, “StackDPPred: A Stacking based
Prediction of DNA-binding Protein from Sequence,” Oxford Bioinformatics, vol.
35, pp. 433–441, 2019.
[3] S. G. Gattani, A. Mishra, and M. T. Hoque, “StackCBPred: A Stacking based
Prediction of Protein-Carbohydrate Binding Sites from Sequence,” Carbohydrate
Research, Elsevier., 2019.
[4] S. Iqbal and M. T. Hoque, “PBRpredict-Suite: A Suite of Models to Predict
Peptide Recognition Domain Residues from Protein Sequence,” Oxford
Bioinformatics 2018
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