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Titlebook: Credit Scoring, Response Modeling, and Insurance Rating; A Practical Guide to Steven Finlay Book 2012Latest edition Palgrave Macmillan, a d

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樓主
發(fā)表于 2025-3-21 18:19:15 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Credit Scoring, Response Modeling, and Insurance Rating
副標(biāo)題A Practical Guide to
編輯Steven Finlay
視頻videohttp://file.papertrans.cn/240/239646/239646.mp4
圖書封面Titlebook: Credit Scoring, Response Modeling, and Insurance Rating; A Practical Guide to Steven Finlay Book 2012Latest edition Palgrave Macmillan, a d
描述A guide on how Predictive Analytics is applied and widely used by organizations such as banks, insurance providers, supermarkets and governments to drive the decisions they make about their customers, demonstrating who to target with a promotional offer, who to give a credit card to and the premium someone should pay for home insurance.
出版日期Book 2012Latest edition
關(guān)鍵詞model construction; modeling; Parameter; performance; Promotion; Rating; Scoring; validation; investments an
版次2
doihttps://doi.org/10.1057/9781137031693
isbn_ebook978-1-137-03169-3
copyrightPalgrave Macmillan, a division of Macmillan Publishers Limited 2012
The information of publication is updating

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https://doi.org/10.1007/978-3-658-03559-4ng a suite of models and then combining the model scores together in some way. The terminology used to describe multi-model systems is diverse and many different terms are used to describe such systems. This includes: model ensembles, fusion systems, model combination and mixture of experts.
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Kollektiva in der Identit?tsentwicklungof observations, should also have been excluded. For individual observations where data is missing or spurious, then a suitable imputation process should have been applied to infer what the true value is likely to have been, or a default value assigned to represent them.
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Sample Selection,model should be as recent as possible to mitigate against changes in the patterns of behavior that accumulate over time. For these reasons it is common for models to be constructed using a sub-set (a sample) of the available data, rather than the full population.
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Gathering and Preparing Data,atabases/data tables/IT systems and held in a variety of different formats. Work is required to extract relevant data from each source, and then match it together to produce a single data set that can be used for data analysis and model construction.
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