Actually I've known about MXnet for weeks as one of the most popular library / packages in Kaggler, but just recently I heard bug fix has been almost done and some friends say the latest version looks stable, so at last I installed it. MXn…

As far as I've known, Xgboost is the most successful machine learning classifier in several competitions in machine learning, e.g. Kaggle or KDD cups. Indeed the team winning Higgs-Boson competition used Xgboost and below is their code rel…

Random Forest is still one of the strongest supervised learning methods although these days many people love to use Deep Learning or Convolutional NN. Of course because it's simple architecture and a lot of implementation in various enviro…

These days almost everybody appears to love a variation of Neural Network (NN) -- Deep Learning. I already argued about how Deep Learning works and what kind of parameters characterizes it in the previous post. What kind of decision bounda…

Actually support vector machine (SVM) is the one that I love the most among various machine learning classifiers... because of its strong generalization and beautiful decision boundary (in high dimensional space). Although there are other …

I think a lot of people love logistic regression because it's pretty light and fast. But we know it's just a linear classifying function -- I mean it's only for linearly separable patterns, not linearly non-separable ones. It's primary ide…

Notice Currently {mvpart} CRAN package was removed from CRAN due to expiration of its support. For installation, 1) please download the latest (but expired) package archive from the old archive site and 2) install it following the procedur…

Below is the most popular post in this blog that recorded an enormous number of PV and received a lot of comments even here or outside this blog. Comparing machine learning classifiers based on their hyperplanes or decision boundaries - Da…