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Comprehensive application of ML algorithms in classification problem

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Classification

Comprehensive application of ML algorithms in classification problem under caret (R) and scikit-learn (Python) frameworks

  • Boosting
    • Adaboost, GBM, XGboost
  • Bagging
    • Random forest
  • Decision tree
    • CART, Conditional inference tree
  • Discriminant analysis
    • LDA, QDA
  • Generalized linear regression
  • K-nearest neighbors
  • Naive Bayes
  • Neural networks
  • Regularization
    • LASSO (L1), Ridge(L2), Elastic net
  • Support vector machines

Data Source

Red wine quality dataset from UCI Machine Learning Repository

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Comprehensive application of ML algorithms in classification problem

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