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README: [Datasets present at the same location as the python scripts]

  1. Run the perceptron.py python file :

    Syntax:​ python perceptron.py --dataset --mode

    -- Dataset:​ linearly-separable-dataset.csv / Breast_cancer_data.csv -- Mode:​ erm / cv (erm - Empirical Risk Minimization, cv - Cross-validation), for cross validation, number of folds ‘k’ = 10 by default)

To run the code:​

python perceptron.py --dataset linearly-separable-dataset.csv --mode erm 				
python perceptron.py --dataset Breast_cancer_data.csv --mode cv
  1. Run the adaBoost.py python file.

    Syntax:​ python adaBoost.py --dataset --mode

    -- Dataset:​ Breast_cancer_data.csv -- Mode:​ erm / cv (For cross validation, number of folds = 10 by default) / plot (for plotting graph between ERM and Validation error vs Rounds)

To run the code:​

(i)   python adaBoost.py --dataset Breast_cancer_data.csv --mode erm 					
(ii)  python adaboost-final.py --dataset Breast_cancer_data.csv --mode cv 					
(iii) python adaboost-final.py --dataset Breast_cancer_data.csv --mode plot    (for plotting graph)

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Perceptron Implementation - implementing Empirical Risk Minimization (ERM) and k-folds cross-validation

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