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01 - Supervised Learning

Algorithms

  1. Decision Tree
  2. Multi-layer Perceptron (Neural Network)
  3. AdaBoost Decision Tree
  4. Support Vector Machines
  5. K-Nearest Neighbors

Problems

  1. Cancer detection
  2. Raisin class

Plots

  1. Learning curve
  2. Validation curve

svm

Metrics

  1. Accuracy
  2. Recall
  3. Log Loss

Instructions

  1. Go to repo.
  2. Click on Code
  3. Click on Download ZIP
  4. Unzip the files
  5. Run the python file experiment1.py
  6. Run the python file experiment2.py

Results

  1. All results will be printed to the console including scores and execution times.
  2. All the 48 graphs will be generated directly in the repository once the files are run successfully.

References

  1. Machine Learning - Tom M. Mitchell, McGrawHill.
  2. Data Science for Business, by Foster Provost, Tom Fawcett
  3. http://archive.ics.uci.edu/ml/datasets/Raisin+Dataset
  4. http://archive.ics.uci.edu/ml/datasets/Haberman%27s+Survival
  5. https://towardsdatascience.com/selecting-the-right-metric-for-skewed-classification-problems-6e0a4a6167a7
  6. https://towardsdatascience.com/performance-metrics-for-binary-classifier-in-simple-words-be958535db49
  7. https://www.projectpro.io/recipes/find-optimal-parameters-using-gridsearchcv
  8. https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/
  9. https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/
  10. https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html
  11. https://datascience.stackexchange.com/questions/4943/intuition-for-the-regularization-parameter-in-svm
  12. https://stackoverflow.com/questions/46616883/how-to-set-numbers-of-epoch-in-scikit-learn-mlpregressor
  13. https://www.datarobot.com/blog/introduction-to-loss-functions/
  14. https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter
  15. https://www.youtube.com/watch?v=p3CcfIjycBA
  16. https://machinelearningmastery.com/understand-the-dynamics-of-learning-rate-on-deep-learning-neural-networks/