Linear-Machine-Learning-Algorithms

Implementation of Perceptron, Winnow and Adagrad-Perceptron, along with their averaged versions on synthetic and real world datasets

Datasets

1. Synthetic Sparse data: training, development, test.
2. Synthetic Dense data: training, development, test.
3. Real data set: Training = CoNLL; Dvelopment = CoNLL and Enron; Test: CoNLLand Enron.

Running the Script

If you run the main function without doing any changes, you will get the following:

1. News Dev Accuracy For Averaged Perceptron
2. Email Dev Accuracy for Averaged Perceptron
3. News Dev Accuracy for SVM
4. Email Dev Accuracy for SVM
5. Accuracies of 7 seven models for sparse and synthetic development data. (14 train - 14 test accuracies are printed)

Each algorithm runs for 10 iterations. This is because averaged versions require some catch up with their normal versions.I talk about this in my report.

Implementation Details:

The implementation of Adagrad, Winnow and Perceptron are all under Classifier class.

If averaged variable is True, average versions of the algorithms are implemented.

Other Functions

Functions	Usage
Plottng Learning Curves of 7 models	Uncomment lines 944-945
Hyperparameter Tuning for Winnow	Uncomment line 937
Hyperparameter Tuning for Adagrad	Uncomment line 936

The pdf file includes all the experiments I did with seven linear learning algorithms.

Thank you for reading, please let me know if anyone has problems running the script.