# 计算机代写|机器学习代写machine learning代考|COMP5318

#### Doug I. Jones

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## 计算机代写|机器学习代写machine learning代考|Classification and the Learning Pipeline

So far, we have considered supervised learning tasks in which the output variable $y$ is a real number, that is, $y \in \mathbb{R}$. Often, we will deal with problems with binary or categorical output variables, for example, we might be interested in problems such as:

• Will a user click on a product or advertisement? (binary outcome)
• What category of object does an image contain? (multiclass)
• What product is a user most likely to purchase next? (multiclass)
• Which of the two products would a user prefer? (binary)
In this chapter, we will explore how to design classification algorithms for tasks like those above, and in particular explore a classifier that extends the ideas behind regression from Chapter 2 to classification problems.

Logistic Regression sets up classification using a probabilistic framework, by transforming the predictions $X \cdot \theta$ that we used when building regressors into probabilities associated with observing a particular label $y$. By associating a probability with a particular label, and thereby to all of the labels in a dataset, we can again develop prediction frameworks that are differentiable and can be optimized using gradient-based approaches, much as we saw in Section 2.5.

Ultimately logistic regression is just one of dozens of classification schemes; we describe it here rather than alternatives (such as Support Vector Machines (Cortes and Vapnik, 1995), or Random Forest Classifiers (Ho, 1995)) mainly because logistic regression more closely matches the approaches we will develop in later chapters. This same type of modeling approach will be used throughout this book, when building Recommender Systems in Chapter 5, or generating fashionable outfits in Chapter 9 , among others. We will briefly discuss the merits of alternative classification approaches in Section 3.2.

After exploring classification techniques in Section 3.1, we will explore evaluation strategies for classification models in Section 3.3, much as we did for regression models in Chapter 2.

Finally, we will explore the idea of the learning pipeline. Having developed techniques for regression (chap. 2), classification, and evaluation strategies for both, in Section $3.4$ we will explore how to compare models, how to ensure that our results are significant, and how to ensure that our models generalize well to unseen data. This type of end-to-end strategy for model training will be used whenever we train supervised learning models throughout the remainder of the book.

## 计算机代写|机器学习代写machine learning代考|Other Classification Techniques

In our introduction to classification, we have only discussed a single classification technique: Logistic Regression. Our choice to explore this particular technique was largely a practical one: the idea of associating a probability with a particular outcome (as in eq. (3.5)) and estimating that probability via a differentiable function (to facilitate gradient ascent) will appear repeatedly as we develop more and more complex models.

However, the technique we have explored is only one class of approach to build classifiers. The specific choice to map binary labels to continuous probabilities via a smooth function has hidden assumptions and limitations, meaning that logistic regression is not the ideal classifier for every situation. Below we present a few alternatives, largely as further reading and to highlight specific situations where logistic regression may not be the preferable choice.

Support Vector Machines: While logistic regressors optimize a probability associated with a set of observed labels, they do not explicitly minimize the number of mistakes made by the classifier. Support Vector Machines (SVMs) (Cortes and Vapnik, 1995) replace the sigmoid function in Figure $3.1$ with an expression that assigns zero cost to correctly classified examples, ${ }^1$ and a positive $\operatorname{cost}^2$ to incorrectly classified examples (in proportion to the confidence of the prediction $x \cdot \theta$ ). This distinction is fairly subtle: while every sample will influence the optimal value of $\theta$ for a logistic regressor, the solution found by an SVM is entirely determined by a few samples closest to the classification boundary, or those that are mislabeled. Conceptually it is appealing for a classifier to focus on the most ‘difficult’ samples in this way, though note that in many cases (and notably when building recommender systems) our goal is to optimize ranking performance rather than classification accuracy (as we will discuss in sec. 3.3.3), such that giving special attention to the most ambiguous examples is not necessarily desirable.

# 机器学习代考

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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