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# 计算机代写|机器学习代写Machine Learning代考|CITS5508 Labels

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## 计算机代写|机器学习代写Machine Learning代考|Labels

Besides its features, a data point might have a different kind of properties. These properties represent a higher-level fact or quantity of interest that is associated with the data point. We refer to such properties of a data point as its label (or “output” or “target”) and typically denote it by $y$ (if it is a single number) or by $\mathbf{y}$ (if it is a vector of different label values, such as in multi-label classification). We refer to the set of all possible label values of data points arising in a ML application is the label space $\mathcal{Y}$. In general, determining the label of a data point is more difficult (to automate) compared to determining its features. Many ML methods revolve around finding efficient ways to predict (estimate or approximate) the label of a data point based solely on its features.

As already mentioned, the distinction of data point properties into labels and features is blurry. Roughly speaking, labels are properties of datapoints that might only be determined with the help of human experts. For datapoints representing humans we could define its label $y$ as an indicator if the person has flu $(y=1)$ or not $(y=0)$. This label value can typically only be determined by a physician. However, in another application we might have enough resources to determine the flu status of any person of interest and could use it as a feature that characterizes a person.

Consider a datapoint that represents some hike, at the start of which the snapshot in Fig. $2.2$ has been taken. The features of this datapoint could be the red, green and blue (RGB) intensities of each pixel in the snapshot in Fig. 2.2. We stack these RGB values into a vector $\mathbf{x} \in \mathbb{R}^n$ whose length $n$ is three times the number of pixels in the image. The label $y$ associated with a datapoint (which represents a hike) could be the expected hiking time to reach the mountain in the snapshot. Alternatively, we could define the label $y$ as the water temperature of the lake visible in the snapshot.
Numeric Labels (Regression). For a given ML application, the label space $\mathcal{Y}$ contains all possible label values of data points. In general, the label space is not just a set of different elements but also equipped (algebraic or geometric) structure. To obtain efficient ML methods, we should exploit such structure. Maybe the most prominent example for such a structured label space are the real numbers $\mathcal{Y}=\mathbb{R}$. This label space is useful for ML applications involving data points with numeric labels that can be modelled by real numbers. ML methods that aim at predicting a numeric label are referred to as regression methods.

## 计算机代写|机器学习代写Machine Learning代考|Scatterplot

Consider datapoints characterized by a single numeric feature $x$ and single numeric label $y$. To gain more insight into the relation between the features and label of a datapoint, it can be instructive to generate a scatterplot as shown in Fig. 1.2. A scatterplot depicts the datapoints $\mathbf{z}^{(i)}=\left(x^{(i)}, y^{(i)}\right)$ in a two-dimensional plane with the axes representing the values of feature $x$ and label $y$.

The visual inspection of a scatterplot might suggest potential relationships between feature $x$ (minimum daytime temperature) and label $y$ (maximum daytime temperature). From Fig. 1.2, it seems that there might be a relation between feature $x$ and label $y$ since data points with larger $x$ tend to have larger $y$. This makes sense since having a larger minimum daytime temperature typically implies also a larger maximum daytime temperature.

To construct a scatterplot for data points with more than two features we can use feature learning methods (see Chap. 9). These methods transform high-dimensional datapoints, having billions of raw features, to three or two new features. These new features can then be used as the coordinates of the datapoints in a scatterplot.

## 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。