標(biāo)題: Titlebook: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R; Frank Emmert-Streib,Salissou Moutari,Matthias Dehm Textbo [打印本頁(yè)] 作者: 積聚 時(shí)間: 2025-3-21 19:06
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作者: calumniate 時(shí)間: 2025-3-21 20:36 作者: 合乎習(xí)俗 時(shí)間: 2025-3-22 04:22
General Error Measures start by introducing four so-called fundamental errors from which most error measures are derived. Then, we discuss 14 different error measures. Finally, we discuss the evaluation of the outcome of a single method and that of multiple methods, showing that such an evaluation is a complex task that 作者: Exterior 時(shí)間: 2025-3-22 06:52
Resampling Methodse different from the other methods presented in this book. As we will see, resampling and subsampling methods allow the generation of “new” data sets from any given data set, which can then be used either for the assessment of a prediction model or for the estimation of parameters.作者: 舊石器時(shí)代 時(shí)間: 2025-3-22 11:33 作者: 祝賀 時(shí)間: 2025-3-22 13:31
Statistical Inferencen reality, a sample of data always has a finite size, any conclusions reached about the population are always uncertain to a degree. The goal of statistics is to quantify the amount of uncertainty around the conclusions that are made based on a sample of data. In general, . is the (systematic) proce作者: 祝賀 時(shí)間: 2025-3-22 21:04 作者: 出處 時(shí)間: 2025-3-22 21:12
Dimension Reductiont or non-informative, which generally hinders the ability of most machine learning algorithms to perform efficiently. A common approach to address these issues is to check whether a low-dimensional structure can be detected within these high-dimensional data. If the answer is yes, then we can identi作者: 諂媚于人 時(shí)間: 2025-3-23 04:31
Classificationuss aspects common to general classification methods. This includes an extension of measures for binary decision-making to multi-class classification problems. As we will see, this extension is not trivial, because the contingency table becomes multi-dimensional when conditioned on different classes作者: myopia 時(shí)間: 2025-3-23 06:42
Hypothesis Testingriginated from statistics, hypothesis testing has complex interdependencies between its procedural components, which makes it hard to thoroughly comprehend. In this chapter, we discuss the underlying logic behind statistical hypothesis testing and the formal meaning of its components and their conne作者: ELUDE 時(shí)間: 2025-3-23 13:21 作者: exhilaration 時(shí)間: 2025-3-23 16:27
Model Selectionels that could be used for a prediction task and the best among them must be chosen. For instance, for a classification problem, we may consider a support vector machine or a decision tree. Similarly, for a regression analysis, there may be different options for the number of predictors of the model作者: 笨拙的你 時(shí)間: 2025-3-23 20:35 作者: champaign 時(shí)間: 2025-3-24 00:58 作者: aphasia 時(shí)間: 2025-3-24 05:22 作者: committed 時(shí)間: 2025-3-24 06:45
Survival Analysiss to the duration until the occurrence of a particular event, while an “event” is a special incident that assumes an application-specific meaning; for example, death, heart attack, wear-out or failure of a product or equipment, divorce, violation of parole, or bankruptcy of a company — to name just 作者: 肉身 時(shí)間: 2025-3-24 10:53 作者: Explosive 時(shí)間: 2025-3-24 15:47 作者: 攀登 時(shí)間: 2025-3-24 20:21 作者: Odyssey 時(shí)間: 2025-3-25 01:44
Elements of Data Science, Machine Learning, and Artificial Intelligence Using R作者: 抵制 時(shí)間: 2025-3-25 03:54 作者: cocoon 時(shí)間: 2025-3-25 11:19
General Prediction Modelsis then used in the presentation of the following chapters. Finally, we discuss a fundamental statistical characteristic that holds for every prediction model. We will see that every output of a prediction model is a random variable.作者: observatory 時(shí)間: 2025-3-25 12:10
Datale if one has a sufficient understanding of the underlying phenomena, the data generation process, and the related experimental measurements. For this reason, we describe in this chapter five different data types and the fields from which they come.作者: Schlemms-Canal 時(shí)間: 2025-3-25 19:08 作者: HEED 時(shí)間: 2025-3-25 23:09 作者: 頂點(diǎn) 時(shí)間: 2025-3-26 01:36
Deep Learningpter, we discuss major architectures of deep neural networks, including deep feedforward neural networks, convolutional neural networks, deep belief networks, autoencoders, and long short-term memory networks.作者: BARB 時(shí)間: 2025-3-26 05:15 作者: Germinate 時(shí)間: 2025-3-26 10:08
Statistical Inferencestics is to quantify the amount of uncertainty around the conclusions that are made based on a sample of data. In general, . is the (systematic) process of making predictions about a population, using data drawn from that population.作者: 一條卷發(fā) 時(shí)間: 2025-3-26 13:34 作者: Hearten 時(shí)間: 2025-3-26 17:30
,Unión Patriótica: the Official Party,is then used in the presentation of the following chapters. Finally, we discuss a fundamental statistical characteristic that holds for every prediction model. We will see that every output of a prediction model is a random variable.作者: 難理解 時(shí)間: 2025-3-26 22:11 作者: definition 時(shí)間: 2025-3-27 01:48
https://doi.org/10.1057/9780230510692, we discuss extended models that allow interaction terms, nonlinearities, or categorical predictors. Finally, we introduce generalized linear models (GLMs), which allow the response variable to have a distribution other than a normal distribution, thus enabling a flexible modeling of the response.作者: 異常 時(shí)間: 2025-3-27 07:01
https://doi.org/10.1007/978-1-349-26804-7ich is a concept introduced by Tikhonov to deal with ill-posed inverse problems. We will see that depending on the mathematical formulation of the regularization, different regression models can be derived. Perhaps the most prominent of these is the least absolute shrinkage and selection operator (LASSO) model.作者: expdient 時(shí)間: 2025-3-27 11:02 作者: 身體萌芽 時(shí)間: 2025-3-27 16:45
,2.7182818284590452353602874713…,ent approaches can be used for defining clustering methods. Also, analyzing the validity of clusters can be quite intricate. However, in this chapter, we focus on clustering methods based on similarity and distance measures.作者: 欲望 時(shí)間: 2025-3-27 21:07 作者: CAPE 時(shí)間: 2025-3-28 00:35 作者: 艱苦地移動(dòng) 時(shí)間: 2025-3-28 05:20 作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-28 09:05
Dimension Reductiontion of the data without a significant loss of information are referred to as dimension reduction (or dimensionality reduction) techniques. In this chapter, we introduce some feature extraction and some feature selection techniques.作者: disciplined 時(shí)間: 2025-3-28 12:12
Model Selectionon. There is a related topic called model assessment. Model selection and model assessment are frequently confused, although each of these topics focuses on a different goal. For this reason, we start our discussion about model selection by clarifying the difference compared to model assessment.作者: 窩轉(zhuǎn)脊椎動(dòng)物 時(shí)間: 2025-3-28 15:17
Textbook 2023nce. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book作者: NEX 時(shí)間: 2025-3-28 22:02
Further Topics, Including Surveying,-nearest neighbor classification, logistic regression, support vector machine, and decision tree) that provide a representative overview of the diverse ideas underlying classification methods widely used in many applications.作者: NOTCH 時(shí)間: 2025-3-29 01:07 作者: BRAWL 時(shí)間: 2025-3-29 03:06 作者: 浪費(fèi)時(shí)間 時(shí)間: 2025-3-29 08:23 作者: 創(chuàng)作 時(shí)間: 2025-3-29 13:17
ata using the R programming language.Includes a full suite oThe textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using th作者: 瑣事 時(shí)間: 2025-3-29 17:29
https://doi.org/10.1007/978-1-84996-086-1stics is to quantify the amount of uncertainty around the conclusions that are made based on a sample of data. In general, . is the (systematic) process of making predictions about a population, using data drawn from that population.作者: 香料 時(shí)間: 2025-3-29 21:53 作者: Coronation 時(shí)間: 2025-3-30 01:33
978-3-031-13341-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: NIB 時(shí)間: 2025-3-30 08:07
portance of this, data have been called the “oil of the twenty-first century.” To deal with this flood of data, a new field called data science has been established. This chapter provides a general overview of data science and what learning from data means.作者: Aphorism 時(shí)間: 2025-3-30 08:21
https://doi.org/10.1007/978-3-030-15002-0 start by introducing four so-called fundamental errors from which most error measures are derived. Then, we discuss 14 different error measures. Finally, we discuss the evaluation of the outcome of a single method and that of multiple methods, showing that such an evaluation is a complex task that requires the interpretation of an analysis.作者: Aqueous-Humor 時(shí)間: 2025-3-30 13:04 作者: 音樂(lè)等 時(shí)間: 2025-3-30 18:04
Introduction to Learning from Data,portance of this, data have been called the “oil of the twenty-first century.” To deal with this flood of data, a new field called data science has been established. This chapter provides a general overview of data science and what learning from data means.作者: sed-rate 時(shí)間: 2025-3-31 00:02 作者: burnish 時(shí)間: 2025-3-31 02:43
Resampling Methodse different from the other methods presented in this book. As we will see, resampling and subsampling methods allow the generation of “new” data sets from any given data set, which can then be used either for the assessment of a prediction model or for the estimation of parameters.作者: colostrum 時(shí)間: 2025-3-31 06:13 作者: Ankylo- 時(shí)間: 2025-3-31 09:36
,Unión Patriótica: the Official Party,s in data science. This will show that there is more than one way to look at prediction models, and that no one is superior to the others. In addition, we present our own pragmatic organization of methods, which consists of a mixture of application-based and model-based categories. Such an approach 作者: Accessible 時(shí)間: 2025-3-31 15:44
https://doi.org/10.1007/978-3-030-15002-0 start by introducing four so-called fundamental errors from which most error measures are derived. Then, we discuss 14 different error measures. Finally, we discuss the evaluation of the outcome of a single method and that of multiple methods, showing that such an evaluation is a complex task that 作者: 發(fā)酵 時(shí)間: 2025-3-31 18:37
Making Strategic Leaders: Some Reflections,e different from the other methods presented in this book. As we will see, resampling and subsampling methods allow the generation of “new” data sets from any given data set, which can then be used either for the assessment of a prediction model or for the estimation of parameters.作者: keloid 時(shí)間: 2025-4-1 00:55 作者: 星星 時(shí)間: 2025-4-1 05:21 作者: 河潭 時(shí)間: 2025-4-1 08:47
