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统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Fraud in Prepaid Subscribers
Support vector machines are useful in pure prediction tasks such as fraud detection. In these cases, a full interpretation of the model outcomes is not necessary, and the main interest is in the classification results or the model decisions. A scenario like this is when a telecommunications company creates a marketing promotion to boost new cell phone operations. The promotion gave subscribers prepaid telephone minutes to make calls at the same amount as the minutes received from any telephone.
Fraudsters immediately identified an opportunity in this scenario. The fraud occurs when the criminals acquire fixed lines to connect to a computer that uses a soft switch that is a call switching node in a telecommunications network. From the soft switch, the criminals generate robocalls to hundreds of cell phones to obtain the prepaid minutes. Of course, the criminals had no intention to also pay for the fixed lines. In this case, it is a double fraud against the telecommunications company. Criminals can also use altered pay phones to generate these calls, and then the prepaid credits, or illegal extensions to switches to do the same, to make calls to prepaid cell phones and generate credits to them. Once these prepaid cell phones are full of credits, the criminals sell these prepaid phones on the black market. In many markets, there is no need to provide any information to the carrier when purchasing prepaid subscriber identification module (SIM cards).
The main goal of the fraud detection model is to detect unusual behavior in receiving calls and block the prepaid minutes credit. Some useful variables include no active behavior in making calls, exceedingly high and consistent behavior in receiving calls, very narrow list of originating numbers, and originating numbers at the same place, sometimes at the same address. The SVM model classifies the cases based on a binary target, 0 or 1 , or in this case, fraud or non-fraud. This classification is based on the posterior probability of the event, or the likelihood of a prepaid fraud. Based on the posterior probability, the prepaid phone numbers with the highest posterior probability are sent to a deny list that cancels their prepaid credits. Fraud detection models can save telecommunications timely monitor the events. They must be closely monitored to assess their performance to predict fraud cases. They are often updated frequently, as fraud events change often. For all these reasons, the main goal for fraud detection models is the performance, rather than the interpretability, of the model. This is a perfect case for neural networks, random forests, gradient boosting, and support vector machines.
统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Factorization Machines
There is a class of business applications that involves estimating how users would rate some items. In this business scenario, very often companies do not have much information about the users nor about the items. The main goal in this type of model is to evaluate relationships between users and items. For example, consider a streaming based company selling on-demand movies to users. This company has little information about its customers (users) except the movies (items) they watch. Users can eventually rate the movies that they watch. If we create a matrix considering users and movies, considering the combination of a rating given by a user to a movie, this final matrix will be very sparse. We can expect, for example, millions of users and hundreds of thousands of movies. Not all customers rate all movies. In fact, few customers rate movies, which leads to a huge matrix comprising a substantial number of missing ratings. In other words, most of the cells in that matrix would be missing. The challenge here is to estimate what would be a user’s rating to a particular movie.
Factorization machines are a new and powerful tool for modeling high-dimensional and sparse data. A sparse matrix is the matrix of users and items of the movie company we just mentioned. The main goal of factorization machines is to predict the missing entries in the matrix. By estimating the missing entries, the company would know all ratings for all movies, considering all users. That information would allow the company to find items that users would give high ratings and then recommend those items to each user (except the ones who have rated those items).
Factorization machine models are used in recommender systems where the aim is to predict user ratings on items. There are two major types of recommender systems. One type relies on the content filtering method. Content filtering assumes that a set of information about the items is available for the model estimation. This set of information is often referred to as side information, and it describes the items that will be recommended. By having the side information available, the content filtering has no problem in estimating the rating for a new item.
The second type is based on collaborative filtering. This method does not require additional information about the items. The information needed is the matrix containing the set of users, the set of items, and the ratings of users to items. The combination of all users and items creates a large, sparse matrix containing lots of missing ratings. The collaborative filtering method works well in estimating ratings for all combinations of users and items. The problem with the collaborative filtering method is the inability to estimate added items that have no ratings.
Figure 5.4 shows an example of collaborative filtering. In this case, all the information the company has is the existing ratings, that can be represented in a matrix. Not all the users rate all the items, so some of the ratings will be missing.
Factorization machines estimate the ratings by summing the average rating over all users and items, the average ratings given by a user, the average ratings given to an item, and a pairwise interaction term that accounts for the affinity between a user and an item. The affinity between users and items is modeled as the inner product between two vectors of features: one for the user and another for the item. These features are collectively known as factors.
统计与机器学习代考
统计代写|统计与机器学习作业代写统计和机器学习代考|预付费用户的欺诈
支持向量机在纯粹的预测任务中很有用，比如欺诈检测。在这些情况下，对模型结果的全面解释是没有必要的，主要的兴趣是分类结果或模型决策。一个类似的场景是，当电信公司创建一个营销推广，以促进新的手机业务。这项促销活动为用户提供了预付费电话通话时间，用户拨打的电话通话时间与其他电话通话时间相同。骗子们立即在这个场景中发现了一个机会。当犯罪分子获取固定线路连接到使用软交换(电信网络中的呼叫交换节点)的计算机时，欺诈就发生了。犯罪分子通过软开关向数百部手机发出语音电话，以获得预付费通话时间。当然，罪犯并没有打算也支付固定线路的费用。在这种情况下，这是对电信公司的双重欺诈。犯罪分子还可以使用改装过的公用电话拨打这些电话，然后使用预付费积分，或非法扩展交换机来做同样的事情，向预付费手机拨打电话，并向它们生成积分。一旦这些预付费手机充值，犯罪分子就会在黑市上出售这些预付费手机。在许多市场，当购买预付费用户识别模块(SIM卡)时，不需要向运营商提供任何信息
欺诈检测模型的主要目标是检测在接听电话时的异常行为，并阻止预付费分钟信用。一些有用的变量包括:打电话时没有主动行为、接电话时行为异常频繁且一致、起始号码列表非常狭窄、起始号码位于同一地点(有时位于同一地址)。SVM模型基于二进制目标(0或1)对案例进行分类，在这种情况下，是欺诈还是非欺诈。这种分类是基于事件的后验概率，或预付欺诈的可能性。根据后验概率，后验概率最高的预付费电话号码被发送到拒绝列表，拒绝列表取消预付费电话号码的预付费积分。欺诈检测模型可以节省电信及时监控的事件。必须密切监测它们的表现，以预测欺诈案件。它们经常更新，因为欺诈事件经常变化。由于所有这些原因，欺诈检测模型的主要目标是模型的性能，而不是可解释性。对于神经网络、随机森林、梯度增强和支持向量机来说，这是一个完美的例子
统计代写|统计与机器学习作业代写统计和机器学习代考|分解机器
有一类业务应用程序涉及估计用户如何评价某些项目。在这种业务场景中，公司通常没有关于用户或项目的太多信息。这类模型的主要目标是评估用户和项目之间的关系。例如，考虑一家基于流媒体的公司向用户出售点播电影。这家公司除了他们看的电影(商品)外，几乎没有关于他们的顾客(用户)的信息。用户最终可以为他们观看的电影打分。如果我们创建一个考虑用户和电影的矩阵，考虑用户对电影的评价的组合，最终的矩阵将非常稀疏。例如，我们可以期待数百万的用户和数十万的电影。并不是所有的顾客都评价所有的电影。事实上，很少有客户给电影打分，这导致了一个由大量缺失评分组成的巨大矩阵。换句话说，基质中的大部分细胞都将消失。这里的挑战是估计用户对某部电影的评价。分解机是对高维稀疏数据建模的一种新的强大工具。稀疏矩阵是我们刚才提到的电影公司的用户和项目的矩阵。因式分解机的主要目的是预测矩阵中缺失的项。通过估计丢失的条目，该公司将知道所有电影的所有评分，并考虑到所有用户。这些信息将使该公司能够找到用户给予高评价的商品，然后将这些商品推荐给每个用户(除了那些给这些商品打分的用户)。在推荐系统中使用分解机模型，其目的是预测用户对项目的评分。推荐系统主要有两种类型。一种类型依赖于内容过滤方法。内容筛选假设关于项目的一组信息可用于模型估计。这组信息通常被称为附加信息，它描述了将被推荐的项目。通过提供可用的附加信息，内容过滤在估计新项目的评级时没有问题。第二类是基于协同过滤的。此方法不需要关于项目的附加信息。所需的信息是包含用户集、项目集和用户对项目的评级的矩阵。所有用户和项目的组合创建了一个包含大量缺失评级的大型稀疏矩阵。协同过滤方法可以很好地估计用户和项目的所有组合的评级。协同过滤方法的问题是无法估计添加的没有评分的项目
图5.4显示了一个协同过滤的例子。在这种情况下，公司拥有的所有信息都是现有的评级，可以用矩阵表示。并不是所有的用户都对所有的项目打分，所以有些评分将会丢失。分解机通过对所有用户和产品的平均评级、用户给出的平均评级、产品给出的平均评级和说明用户和产品之间亲和力的成对交互项相加来估计评级。用户和物品之间的亲和度被建模为两个特征向量之间的内积:一个用于用户，另一个用于物品。这些特征统称为因子
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