標(biāo)題: Titlebook: Machine Learning Using R; Karthik Ramasubramanian,Abhishek Singh Book 20171st edition Karthik Ramasubramanian and Abhishek Singh 2017 Mach [打印本頁] 作者: 贖罪 時(shí)間: 2025-3-21 16:46
書目名稱Machine Learning Using R影響因子(影響力)
書目名稱Machine Learning Using R影響因子(影響力)學(xué)科排名
書目名稱Machine Learning Using R網(wǎng)絡(luò)公開度
書目名稱Machine Learning Using R網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning Using R被引頻次
書目名稱Machine Learning Using R被引頻次學(xué)科排名
書目名稱Machine Learning Using R年度引用
書目名稱Machine Learning Using R年度引用學(xué)科排名
書目名稱Machine Learning Using R讀者反饋
書目名稱Machine Learning Using R讀者反饋學(xué)科排名
作者: BRAWL 時(shí)間: 2025-3-22 00:06 作者: FEIGN 時(shí)間: 2025-3-22 02:30 作者: 得罪人 時(shí)間: 2025-3-22 06:21 作者: Enteropathic 時(shí)間: 2025-3-22 11:07
Book 20171st editionto learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in?this book?makes it easy for someone to connect the dots...What You‘ll Learn?.Use the model building process fl作者: 樂章 時(shí)間: 2025-3-22 14:00 作者: follicular-unit 時(shí)間: 2025-3-22 20:43 作者: 高腳酒杯 時(shí)間: 2025-3-22 23:03 作者: cleaver 時(shí)間: 2025-3-23 01:34 作者: violate 時(shí)間: 2025-3-23 07:01
Feature Engineering,n easy-to-use guide of key terms and methodology used in feature engineering. The chapter will give due weight to the domain knowledge and some common business limitations while using machine learning algorithms to solve business problems.作者: Grasping 時(shí)間: 2025-3-23 11:38 作者: 吞吞吐吐 時(shí)間: 2025-3-23 15:47 作者: AROMA 時(shí)間: 2025-3-23 18:49 作者: 雪白 時(shí)間: 2025-3-23 22:34
Feature Engineering,ature engineering is a new term coined recently to give due importance to the domain knowledge required to select sets of features for machine learning algorithms. It is one of the reasons that most of the machine learning professionals call it an informal process. In this chapter, we will provide a作者: 公社 時(shí)間: 2025-3-24 06:06
Model Performance Improvement,lso play the role of thresholds to decide whether the model can be put into actual decision making systems or needs improvements. In the previous chapter, we discussed some performance metrics for our continuous and discrete cases. In this chapter, we will discuss how changing the modeling process c作者: 平靜生活 時(shí)間: 2025-3-24 10:08
Scalable Machine Learning and Related Technologies,cture, data, and real-world application. Machine learning was being much talked about in the research community of academia or in well-funded industry research labs. A prototype of any real-world application using machine learning was considered a big feat and a demonstration of breakthrough researc作者: Invigorate 時(shí)間: 2025-3-24 10:50 作者: Hypopnea 時(shí)間: 2025-3-24 16:48
Karthik Ramasubramanian,Abhishek Singhibt Josef M. Fersch. Er entwirft ein neues Modell zur Mitarbeiterbeurteilung und -entwicklung und beschreibt anhand zahlreicher Firmenbeispiele dessen praktische Umsetzung. Au?erdem stellt er das 360°-Feedbacksystem ausführlich und anwendungsorientiert dar..."Erfolgreiche Unternehmen ben?tigen als w作者: HEPA-filter 時(shí)間: 2025-3-24 21:01
Karthik Ramasubramanian,Abhishek Singhordern Objektivit?t und setzen Werte/Ma?st?be voraus. Wie sich beides effektiv verbinden l?sst, beschreibt Josef M. Fersch. Er entwirft ein neues Modell zur Mitarbeiterbeurteilung und -entwicklung und beschreibt anhand zahlreicher Firmenbeispiele dessen praktische Umsetzung. Au?erdem stellt er das 3作者: Anonymous 時(shí)間: 2025-3-25 03:00 作者: 獨(dú)輪車 時(shí)間: 2025-3-25 03:21
odellierung. Die Gründe für diese Vorgehensweise liegen auf der Hand – man erhofft sich aus der Kenntnis der Lokalit?ten, den Subsystemen, ein besseres Verst?ndnis für die Ursachen der Leistung des ganzen Systems. Dementsprechend haben wir erst lokale Leistungsgesetze abgeleitet, gültig nur für Teil作者: 單色 時(shí)間: 2025-3-25 08:22 作者: depreciate 時(shí)間: 2025-3-25 13:43 作者: 我吃花盤旋 時(shí)間: 2025-3-25 17:14 作者: CYT 時(shí)間: 2025-3-25 23:13 作者: COM 時(shí)間: 2025-3-26 01:29 作者: 半圓鑿 時(shí)間: 2025-3-26 07:34
Sampling and Resampling Techniques,Sampling is an important block in our machine learning process flow and it serves the dual purpose of cost savings in data collection and reduction in computational cost without compromising the power of the machine learning model.作者: MONY 時(shí)間: 2025-3-26 12:15 作者: 熒光 時(shí)間: 2025-3-26 14:19
Machine Learning Model Evaluation,In many cases, we may even discard the complete model based on the performance metrics. This phase of the PEBE plays a very critical role in the success of any ML based projects.作者: cancellous-bone 時(shí)間: 2025-3-26 19:04 作者: 巨碩 時(shí)間: 2025-3-26 23:24 作者: 輕浮女 時(shí)間: 2025-3-27 02:05
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