標(biāo)題: Titlebook: Assessing and Improving Prediction and Classification; Theory and Algorithm Timothy Masters Book 2018 Timothy Masters 2018 prediction.class [打印本頁] 作者: 不友善 時間: 2025-3-21 19:17
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作者: 生存環(huán)境 時間: 2025-3-21 22:01 作者: BUST 時間: 2025-3-22 00:26
Resampling for Assessing Parameter Estimates,r (or several numbers) that result from the interaction. For example, we may use the random sample to train a model and then examine one or more of the model’s learned parameters. More often, we apply a previously trained model to the cases and compute a measure of the model’s performance so that we作者: 沒有希望 時間: 2025-3-22 05:20
Resampling for Assessing Prediction and Classification,model is trained with one dataset, the training set, and then tested with a completely independent dataset, the test set or validation set or out-of-sample set. (The choice of term is usually personal preference.) A performance measure, such as mean squared error, median absolute error, profit facto作者: Mnemonics 時間: 2025-3-22 12:47
Miscellaneous Resampling Techniques,s be estimated. This included model parameters as well as performance measures based on independent test data. Then in Chapter 4 we saw that performance measures for a model could be safely obtained from the very same data that was used to train the model. In this chapter we will explore assorted me作者: Gustatory 時間: 2025-3-22 15:55
Combining Numeric Predictions,or the final use. But there is usually a better approach: keep and use many or all of the models. In all likelihood, some models will have weaknesses that can be alleviated by the strengths of others. By intelligently combining the predictions made by multiple models, a consensus prediction can be m作者: phase-2-enzyme 時間: 2025-3-22 20:50 作者: 鬧劇 時間: 2025-3-22 21:39
Gating Methods,rder to obtain an opinion superior to what could be obtained from a single model. These models may be numeric predictors or classifiers. What distinguishes the techniques of this chapter from the model combination methods described in prior chapters is that a gated combiner requires the use of a ded作者: 一瞥 時間: 2025-3-23 04:02 作者: 饒舌的人 時間: 2025-3-23 05:38
Food Adulteration and Authenticityone of several competing categories (benign versus malignant, tank versus truck versus rock, and so forth). In numeric prediction, the goal is to assign a specific numeric value to a case (expected profit of a trade, expected yield in a chemical batch, and so forth). Actually, such a clear distincti作者: Epidural-Space 時間: 2025-3-23 10:21
https://doi.org/10.1007/978-3-319-39253-0od that the distinction is not always clear. In particular, almost no models can be considered to be pure classifiers. Most classification models make a numeric prediction (of a scalar or a vector) and then use this numeric prediction to define a classification decision. Thus, the real distinction i作者: Esophagitis 時間: 2025-3-23 16:15 作者: 不可知論 時間: 2025-3-23 21:16 作者: Occipital-Lobe 時間: 2025-3-23 22:31
https://doi.org/10.1007/978-0-387-33957-3s be estimated. This included model parameters as well as performance measures based on independent test data. Then in Chapter 4 we saw that performance measures for a model could be safely obtained from the very same data that was used to train the model. In this chapter we will explore assorted me作者: 宣稱 時間: 2025-3-24 03:48 作者: 檢查 時間: 2025-3-24 07:25
Tsvetko Prokopov,Stoyan Tanchevsions on numeric predictions, the methods of that chapter are often a good choice. However, some models are inherently strict classifiers in that they produce a class decision and nothing more. Also, many number-based classifiers produce numeric predictions that are unstable in some way. In such sit作者: 記憶 時間: 2025-3-24 12:31 作者: inveigh 時間: 2025-3-24 17:53
Preventive Measures for Food Safetyl in some way. But even the most sophisticated model is helpless if it is not given the information it needs to make a good decision. In this chapter, we explore the concept of information content of a variable, and we present a variety of algorithms for assessing the amount and nature of this infor作者: Ischemic-Stroke 時間: 2025-3-24 22:00
https://doi.org/10.1007/978-1-4842-3336-8prediction; classification; assess; improve; AI; artificial; intelligence; big data; analytics; statistics; an作者: endure 時間: 2025-3-25 00:28
Information and Entropy,l in some way. But even the most sophisticated model is helpless if it is not given the information it needs to make a good decision. In this chapter, we explore the concept of information content of a variable, and we present a variety of algorithms for assessing the amount and nature of this information.作者: 招惹 時間: 2025-3-25 04:29 作者: Antagonism 時間: 2025-3-25 07:33
Timothy MastersAn expert-driven practical book based on real-life assessment examples of performance and classification models.Rich with C++ code examples and analysis of data.Contains all you need to know to analyz作者: 催眠 時間: 2025-3-25 14:26
http://image.papertrans.cn/b/image/163296.jpg作者: 換話題 時間: 2025-3-25 19:47
Assessing and Improving Prediction and ClassificationTheory and Algorithm作者: 安撫 時間: 2025-3-25 23:18
Assessing and Improving Prediction and Classification978-1-4842-3336-8作者: 面包屑 時間: 2025-3-26 03:34 作者: GULP 時間: 2025-3-26 06:34 作者: organic-matrix 時間: 2025-3-26 08:48 作者: 準(zhǔn)則 時間: 2025-3-26 15:49 作者: 即席演說 時間: 2025-3-26 20:24 作者: 背信 時間: 2025-3-27 00:20 作者: Annotate 時間: 2025-3-27 03:07 作者: 態(tài)度暖昧 時間: 2025-3-27 06:01 作者: 陶器 時間: 2025-3-27 10:28 作者: ineffectual 時間: 2025-3-27 16:13
Armando Venancio,Russell Patersonthat can be alleviated by the strengths of others. By intelligently combining the predictions made by multiple models, a consensus prediction can be made that is nearly always superior to that made by the single best model. This chapter discusses a variety of methods for combining numeric predictions.作者: 幻影 時間: 2025-3-27 20:10 作者: 怒目而視 時間: 2025-3-27 22:47 作者: TIA742 時間: 2025-3-28 02:42 作者: 輕浮女 時間: 2025-3-28 08:48
Gating Methods,ishes the techniques of this chapter from the model combination methods described in prior chapters is that a gated combiner requires the use of a dedicated variable or variables to control the combination process, while the previously discussed techniques operate without the benefit of such outside help.作者: abracadabra 時間: 2025-3-28 11:15 作者: Outshine 時間: 2025-3-28 16:34
Food Adulteration and Authenticityr costs obtained from a classification scheme. The subject of this chapter is somewhat different, though nevertheless related. Here, we are not concerned with performance on individual or small groups of future cases. Rather, we compute a single measure that describes some aspect of the model, and then we judge the quality of this measurement.作者: 貝雷帽 時間: 2025-3-28 22:38 作者: cunning 時間: 2025-3-29 00:01 作者: Stable-Angina 時間: 2025-3-29 06:08 作者: Intervention 時間: 2025-3-29 10:07
Resampling for Assessing Prediction and Classification,r, cost, or any other custom figure, is computed for the independent set. This performance measure provides a quality judgment for the model. Naive researchers stop here. But enlightened researchers go one step further. They use the methods described in Chapter 3 to evaluate the reliability of the performance measure.作者: PANT 時間: 2025-3-29 14:17
https://doi.org/10.1007/978-3-030-01340-0political scandal; pop culture; comedy; presidents; Presidential Scandal; corruption; Congress; Trump; Water作者: 俗艷 時間: 2025-3-29 18:02
https://doi.org/10.1007/978-3-642-84703-5Animation; Anwendungen; Architektur; Base; Bauingenieurwesen; CIP (Computer-Investitions-Programm); Comput
