Boosting - EXPLAINED!

REFERENCES [1] A Short Introduction to Boosting: https://cseweb.ucsd.edu/~yfreund/papers/IntroToBoosting.pdf [2] A Theory of the Learnable (Valiant, 1984): http://web.mit.edu/6.435/www/Valiant84.pdf. This introduced the PAC Learning model [3] PAC Learning Model: https://www.youtube.com/watch?v=mztE3UHA8DU [4] Cryptographic Limitations on Learning Boolean Formulae & Finite Automata (Kearns et al., 1988): https://www.cis.upenn.edu/~mkearns/papers/crypto.pdf (This paper defined weak learnability) [5] The strength of weak learnability (Schapire, 1990): http://rob.schapire.net/papers/strengthofweak.pdf [6] A gentle intro to weak learners: https://www.cs.ox.ac.uk/people/varun.kanade/teaching/AML-HT2017/lectures/lecture04.pdf [7] Boosting a weak learning algorithm by majority (Freund, 1995): https://pdfs.semanticscholar.org/d620/946e24eee13bc3bdd5ceb0f90a3dc4bc4a54.pdf [8] Adaptive Boosting (Section 4): http://rob.schapire.net/papers/FreundSc95.pdf [9] Adaboost & overfitting discussion: https://stats.stackexchange.com/questions/20622/is-adaboost-less-or-more-prone-to-overfitting [10] Gradient Boosting: https://statweb.stanford.edu/~jhf/ftp/trebst.pdf [11] How boosting still learns even after training error hits 0: https://www.cc.gatech.edu/~isbell/tutorials/boostingmargins.pdf [12] Difference between Adaboost & Gradient Boost: https://www.quora.com/What-is-the-difference-between-gradient-boosting-and-adaboost [13] Adaboost Vs Gradient Boosting: https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788295758/4/ch04lvl1sec34/comparison-between-adaboosting-versus-gradient-boosting [14] XGBoost (Main Paper): https://arxiv.org/abs/1603.02754 [15] Compressed Sparse Column (CSC) format used in storing data in XGboost: https://software.intel.com/en-us/mkl-developer-reference-c-sparse-blas-csc-matrix-storage-format CODE [1] Starter code with built in libraries: https://repl.it/@PulkitSharma1/Boosting-algorithms IMAGE RESOURCES [1] ConvNet: https://miss