best out-of-the-box solution Random Forests Scikit-Learn, randomForest Extra Trees Scikit-Learn Regularized Greedy Forest Tong Zhang’s Neural Networks Keras, Lasagne, MXNet Blends well with GBM. Best at image recognition competitions. Logistic/Linear Regression Scikit-Learn, Vowpal Wabbit Fastest. Good for ensemble. Support Vector Machine Scikit-Learn FTRL Vowpal Wabbit, tinrtgu’s Competitive solution for CTR estimation competitions Factorization Machine libFM Winning solution for KDD Cup 2012 Field-aware Factorization Machine libFFM Winning solution for CTR estimation competitions
Neural Network 14 Factorization Machine 12 Logistic Regression 6 Kernel Ridge Regression 2 Extra Trees 2 Random Forest 2 K-Nearest Neighbor 1 • A total of 64 single models were used in the final solution.
0.907765 Stage-I Best 0.907688 0.908796 Stage-II Best 0.907968 N/A Stage-III Best 0.908194 0.909181 • Single best to Stage-III ensemble best score is 0.0014 improvement!