Interpretation of the Confusion Matrix

Actual Positive, Predicted Positive = TP Actual Positive, Predicted Negative = FN Actual Negative, Predicted Negative = TN Actual Negative, Predicted Positive = FP We know from the sample, Actual Positive = TP + FN Actual Negative = TN + FP Prevalence: If the sample can present the population, then prevalence is the ratio: Actual …

The Math behind Linear SVC Classifier

This article is reposted from my Kaggle account. Here is the original link: https://www.kaggle.com/xingewang/the-math-behind-linear-svc-classifier In case there are some Latex format altered below. Linear Support Vector Classifier (Binary Case) The reason why I wrote this article: explanation of SVC/SVM on the internet is overwhelming, but I could not find anything to clarify all the doubts I …

Bootstrapping

Problem address In real life, we draw samples from our interested population, and use our samples to estimate the features of the targeted population. However, sometimes, getting samples can be expensive and time consuming. How can we know if the estimators we get from our samples are accurate for the population or not? How can …

NMF Algorithm

http://nmf.r-forge.r-project.org/demo-heatmaps.html R basismap() of the NMF. http://blog.csdn.net/waycaiqi/article/details/11734735 http://liuzhiqiangruc.iteye.com/blog/2095116 http://techtrace.org/?p=2104