supplementary數學的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列免費下載的地點或者是各式教學

Tree-Based Methods: A Practical Introduction with Applications in R

為了解決supplementary數學的問題，作者Greenwell, Brandon M. 這樣論述：

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong fou

ndation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology.The book follows up most ideas and mathematical concepts with code-based examples in the R s

tatistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core c

hapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers.Consumers of this book w

ill have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work.Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees,

bagging, boosting, and random forests).A companion website containing additional supplementary material and the code to reproduce every example and figure in the book.A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an im

plementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree).Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLli

b (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.

supplementary數學進入發燒排行的影片

高中學生與教師對目標語作為教學語言之觀點探究

為了解決supplementary數學的問題，作者陳怡文 這樣論述：

本研究旨在探究高中學生與教師對目標語作為英語課室之教學語言的觀點。研究對象為來自台灣北部偏鄉一所國立高中的十四名高中生與一名教師。為深入了解學生與教師的觀點，本研究採用質性研究法，資料蒐集包括開放式問卷以及半結構訪談。本研究主要發現如下:一、支持性的學習環境有助於教學語言的轉換，其中良好的師生關係與同儕的合作有關鍵性的影響。二、儘管老師與多數學生支持以目標語作為教學語言，為確保學生的理解，課堂中仍需以母語加以輔助。三、使用目標語作為教學語言提供學生英語使用的範例與應用英語的機會，有助於提升學生英語能力，發展學生對英語學習之正向態度；並使其了解學習英語之價值與重要性。最後，依據上述各項發現，構

成一套理論模式-「目標語作為高中英語課室之教學語言金字塔」(Pyramid of the Use of Target Language Instruction in Senior High School English Classrooms)，具體解釋目標語作為高中英語課室之教學語言的要素。整體而言，學生與教師支持以目標語作為高中英語課室的主要教學語言，並在必要時適時以母語作為輔助。

Bayesian Networks: With Examples in R

為了解決supplementary數學的問題，作者Scutari, Marco,Denis, Jean-Baptiste 這樣論述：

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the s

implest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.The first three chapters explain the whole process of

Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random v

ariables (using Stan). The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R packages and other software implementing Bayesian networks. The final chapter evaluates two r

eal-world examples: a landmark causal protein-signalling network published in Science and a probabilistic graphical model for predicting the composition of different body parts.Covering theoretical and practical aspects of Bayesian networks, this book provides you with an introductory overview of th

e field. It gives you a clear, practical understanding of the key points behind this modelling approach and, at the same time, it makes you familiar with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields, data-driv

en models and expert systems, probabilistic and causal perspectives, thus giving you a starting point to work in a variety of scenarios.Online supplementary materials include the data sets and the code used in the book, which will all be made available from https: //www.bnlearn.com/book-crc-2ed/

補習班服務品質、顧客滿意度與顧客忠誠度之研究

為了解決supplementary數學的問題，作者陳柏羲 這樣論述：

近年來由於少子化的嚴重衝擊，補習班的經營有著莫大的挑戰和危機，而教育體制的變革，也讓補習班的發展愈加困難，再加上補習班四處林立、蓬勃發展，營運的競爭更是相當激烈。因此如何在茫茫學海中求得立基和成長，進而永續經營，這是相當重要的課題。 本研究首先探討PZB模式服務品質的五項構面對顧客滿意度的影響程度，再探究顧客滿意度對顧客忠誠度的影響程度，最後剖析調節變數中不同的班別及年級背景下對顧客滿意度與顧客忠誠度之間關係的影響程度。 以文獻為基礎發展問卷，修訂引用的衡量題項，其中包括服務品質五項構面、顧客滿意度與顧客忠誠度，針對國中小補習班的學生家長為抽樣對象，最終共計回收紙本與電子

有效問卷475份。再透過SmartPLS 3.3.7版統計軟體進行數據處理，採用敘述性統計分析、信度分析、效度分析、結構方程模式及多群組比較分析等統計分析方法。 經由實證結果分析發現，補習班服務品質的有形性、保證性與體貼性會正向影響顧客滿意度，而可靠性與回應性對顧客滿意度的影響不顯著。多群組比較中，在有形性、保證性及體貼性對顧客滿意度與顧客忠誠度之關係的影響上，有就讀較高年級比沒有就讀較高年級者強；但可靠性及回應性對顧客滿意度與顧客忠誠度之關係的影響上，有無就讀高年級的干擾效果不顯著。在有形性及體貼性對顧客滿意度與顧客忠誠度之關係的影響上，有就讀較低年級比沒有就讀較低年級者強；但可靠性、

回應性及保證性對顧客滿意度與顧客忠誠度之關係的影響上，有無就讀低年級的干擾效果不顯著。在就讀科目中，有形性、可靠性、回應性及體貼性對顧客滿意度與顧客忠誠度之關係的影響上，就讀英數班比就讀課輔班者強，而僅在保證性對顧客滿意度與顧客忠誠度之關係的影響上，就讀課輔班比就讀英數班者強，其餘皆呈現不顯著的結果。關鍵詞：補習班、服務品質、顧客滿意度、顧客忠誠度