標(biāo)題: Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical [打印本頁] 作者: Braggart 時(shí)間: 2025-3-21 19:26
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
作者: colony 時(shí)間: 2025-3-22 00:03 作者: excursion 時(shí)間: 2025-3-22 02:37 作者: 晚來的提名 時(shí)間: 2025-3-22 05:15 作者: 遭遇 時(shí)間: 2025-3-22 12:42
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作者: Tdd526 時(shí)間: 2025-3-22 13:11
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作者: Tdd526 時(shí)間: 2025-3-22 17:55 作者: prosperity 時(shí)間: 2025-3-23 00:39 作者: entail 時(shí)間: 2025-3-23 04:40
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作者: Notorious 時(shí)間: 2025-3-23 08:58
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 作者: 無政府主義者 時(shí)間: 2025-3-23 10:14 作者: 一瞥 時(shí)間: 2025-3-23 15:42 作者: Crohns-disease 時(shí)間: 2025-3-23 20:35
Model Performance Assessment,ssarily imply that the model is perfect or that it will reproduce when tested on external data. We need additional metrics to evaluate the model performance and to make sure it is robust, reproducible, reliable, and unbiased.作者: Rodent 時(shí)間: 2025-3-23 23:07
Improving Model Performance,uations, we derive models by estimating model coefficients or parameters. The main question now is . Are there reasons to believe that such . of forecasting methods may actually improve the performance (e.g., increase prediction accuracy) of the resulting consensus meta-algorithm? In this chapter, w作者: 性學(xué)院 時(shí)間: 2025-3-24 04:01 作者: 全國性 時(shí)間: 2025-3-24 09:22
Variable/Feature Selection,more features than observations. Variable selection, or feature selection, can help us focus only on the core important information contained in the observations, instead of every piece of information. Due to presence of intrinsic and extrinsic noise, the volume and complexity of big health data, an作者: Antecedent 時(shí)間: 2025-3-24 12:08
Regularized Linear Modeling and Controlled Variable Selection,the number of cases (.). In such situations, parameter estimates are difficult to compute or may be unreliable as the system is underdetermined. Regularization provides one approach to improve model reliability, prediction accuracy, and result interpretability. It is based on augmenting the primary 作者: jet-lag 時(shí)間: 2025-3-24 18:53 作者: Concrete 時(shí)間: 2025-3-24 19:00 作者: 我沒有強(qiáng)迫 時(shí)間: 2025-3-25 02:29 作者: 博識(shí) 時(shí)間: 2025-3-25 05:45
Apriori Association Rules Learning,stery behind recommendation systems based on transactional records. Specifically, we will (1) discuss association rules and their support and confidence; (2) the . . for association rule learning; and (3) cover step-by-step a set of case-studies, including a toy example, Head and Neck Cancer Medications, and Grocery purchases.作者: Capture 時(shí)間: 2025-3-25 10:28 作者: 預(yù)定 時(shí)間: 2025-3-25 14:46
Motivation,cessing, interrogating, analyzing, and interpreting complex health and biomedical datasets. Readers that finish this textbook and successfully complete the examples and assignments will gain unique skills and acquire a tool-chest of methods, software tools, and protocols that can be applied to a broad spectrum of Big Data problems.作者: 公司 時(shí)間: 2025-3-25 17:13
Data Visualization,s well as strategies for displaying trees, more general graphs, and 3D surface plots. Many of these are also used throughout the textbook in the context of addressing the graphical needs of specific case-studies.作者: 背叛者 時(shí)間: 2025-3-25 21:59 作者: 為現(xiàn)場(chǎng) 時(shí)間: 2025-3-26 00:50 作者: 行乞 時(shí)間: 2025-3-26 05:24 作者: 推遲 時(shí)間: 2025-3-26 12:14 作者: 行為 時(shí)間: 2025-3-26 16:25
Christopher F. Baum,Basma Bekdache, range); (2) explore simple plots; (3) demonstrate the uniform and normal distributions; (4) contrast numerical and categorical types of variables; (5) present strategies for handling incomplete (missing) data; and (6) show the need for cohort-rebalancing when comparing imbalanced groups of subjects, cases or units.作者: 陰謀小團(tuán)體 時(shí)間: 2025-3-26 18:00
https://doi.org/10.1007/978-3-662-55565-1be support vector machine (SVM) classification; and (4) complete several case-studies, including optical character recognition (OCR), the Iris flowers, Google Trends and the Stock Market, and Quality of Life in chronic disease.作者: Root494 時(shí)間: 2025-3-27 00:26 作者: maladorit 時(shí)間: 2025-3-27 02:45
Notes on Softening and Local Instabilitye are going to introduce ways that we can search for optimal parameters for a single ML method as well as aggregate different methods into . to enhance their collective performance relative to any of the individual methods part of the meta-aggregate.作者: intertwine 時(shí)間: 2025-3-27 08:31
Textbook 20181st edition technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation作者: Fibrinogen 時(shí)間: 2025-3-27 11:40 作者: Commemorate 時(shí)間: 2025-3-27 15:53 作者: ARIA 時(shí)間: 2025-3-27 20:29
Towards Instructable Connectionist Systemss well as strategies for displaying trees, more general graphs, and 3D surface plots. Many of these are also used throughout the textbook in the context of addressing the graphical needs of specific case-studies.作者: Radiculopathy 時(shí)間: 2025-3-27 23:27 作者: 長處 時(shí)間: 2025-3-28 04:42 作者: archetype 時(shí)間: 2025-3-28 09:02
https://doi.org/10.1007/978-3-642-82093-9ata objects. In addition, we illustrate SQL server queries, describe protocols for managing, classifying and predicting outcomes from data streams, and demonstrate strategies for optimization, improvement of computational performance, parallel (MPI) and graphics (GPU) computing.作者: JOG 時(shí)間: 2025-3-28 13:25 作者: AER 時(shí)間: 2025-3-28 17:17 作者: 言外之意 時(shí)間: 2025-3-28 20:25 作者: doxazosin 時(shí)間: 2025-3-29 02:14 作者: 同時(shí)發(fā)生 時(shí)間: 2025-3-29 07:02 作者: lactic 時(shí)間: 2025-3-29 10:27 作者: 蒸發(fā) 時(shí)間: 2025-3-29 12:38
ainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi978-3-030-10187-9978-3-319-72347-1作者: Infantry 時(shí)間: 2025-3-29 16:58
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.作者: 畢業(yè)典禮 時(shí)間: 2025-3-29 20:21
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” variables as functions of the old features.作者: Rankle 時(shí)間: 2025-3-30 03:16 作者: insipid 時(shí)間: 2025-3-30 07:35 作者: Albumin 時(shí)間: 2025-3-30 09:15 作者: nerve-sparing 時(shí)間: 2025-3-30 12:36 作者: Induction 時(shí)間: 2025-3-30 20:21 作者: headlong 時(shí)間: 2025-3-30 21:03 作者: abreast 時(shí)間: 2025-3-31 01:46
Christopher F. Baum,Basma Bekdacheferent types of data. Specifically, we will illustrate common . data structures and strategies for loading (ingesting) and saving (regurgitating) data. In addition, we will (1) present some basic statistics, e.g., for measuring central tendency (mean, median, mode) or dispersion (variance, quartiles作者: 合法 時(shí)間: 2025-3-31 05:46 作者: coagulation 時(shí)間: 2025-3-31 09:49 作者: Override 時(shí)間: 2025-3-31 14:12 作者: Statins 時(shí)間: 2025-3-31 21:21 作者: 性別 時(shí)間: 2025-4-1 00:35 作者: 影響深遠(yuǎn) 時(shí)間: 2025-4-1 04:30
Towards a Philosophy of Computer Scienceles. 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作者: giggle 時(shí)間: 2025-4-1 09:48 作者: Monotonous 時(shí)間: 2025-4-1 12:46
https://doi.org/10.1007/978-3-662-55565-1, efficient algorithms and reliable software packages have been developed to utilize them for various practical applications. We will (1) describe Neural Networks as analogues of biological neurons; (2) develop hands-on a neural net that can be trained to compute the square-root function; (3) descri
