Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical

Braggart

書目名稱Data Science and Predictive Analytics影響因子(影響力)




書目名稱Data Science and Predictive Analytics影響因子(影響力)學(xué)科排名




書目名稱Data Science and Predictive Analytics網(wǎng)絡(luò)公開度




書目名稱Data Science and Predictive Analytics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science and Predictive Analytics被引頻次




書目名稱Data Science and Predictive Analytics被引頻次學(xué)科排名




書目名稱Data Science and Predictive Analytics年度引用




書目名稱Data Science and Predictive Analytics年度引用學(xué)科排名




書目名稱Data Science and Predictive Analytics讀者反饋




書目名稱Data Science and Predictive Analytics讀者反饋學(xué)科排名

colony

excursion

晚來的提名

遭遇

Linear Algebra & Matrix Computing,is generally challenging to visualize complex data, e.g., large vectors, tensors, and tables in n-dimensional Euclidian spaces (.?≥?3). Linear algebra allows us to mathematically represent, computationally model, statistically analyze, synthetically simulate, and visually summarize such complex data

Tdd526

Dimensionality Reduction,ber of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variable

Tdd526

prosperity

entail

Decision Tree Divide and Conquer Classification,les. In some cases, we need to specify well stated rules for our decisions, just like a scoring criterion for driving ability or credit scoring for loan underwriting. The decisions in many situations actually require having a clear and easily understandable decision tree to follow the classification

Notorious

Forecasting Numeric Data Using Regression Models, this Chapter, we will focus on specific model-based statistical methods providing forecasting and classification functionality. Specifically, we will (1) demonstrate the predictive power of multiple linear regression; (2) show the foundation of regression trees and model trees; and (3) examine two