## 计算机代写|机器学习代写machine learning代考|STAT3888

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

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

The classification results obtained for all the UCI data sets considering the different ECOC configurations are shown in Table 2.2. In order to compare the performances provided for each strategy, the table also shows the mean rank of each ECOC design considering the twelve different experiments. The rankings are obtained estimating each particular ranking $r_i^j$ for each problem $i$ and each ECOC configuration $j$, and computing the mean ranking $R$ for each design as $R_j=\frac{1}{N} \sum_i r_i^j$, where $N$ is the total number of data sets. We also show the mean number of classifiers (#) required for each strategy.

In order to analyze if the difference between ranks (and hence, the methods) is statistically significant, we apply a statistical test. In order to reject the null hypothesis (which implies no significant statistical difference among measured ranks and the mean rank), we use the Friedman test. The Friedman statistic value is computed as follows:
$$X_F^2=\frac{12 N}{k(k+1)}\left[\sum_j R_j^2-\frac{k(k+1)^2}{4}\right] .$$
In our case, with $k=4$ ECOC designs to compare, $X_F^2=-4.94$. Since this value is rather conservative, Iman and Davenport proposed a corrected statistic:
$$F_F=\frac{(N-1) X_F^2}{N(k-1)-X_F^2}$$

Applying this correction we obtain $F_F=-1.32$. With four methods and twelve experiments, $F_F$ is distributed according to the $F$ distribution with 3 and 33 degrees of freedom. The critical value of $F(3,33)$ for 0.05 is 2.89 . As the value of $F_F$ is no higher than 2.98 we can state that there is no statistically significant difference among the ECOC schemes. This means that all four strategies are suitable in order to deal with multi-class categorization problems. This result is very satisfactory and encourages the use of the compact approach since similar (or even better) results can be obtained with far less number of classifiers. Moreover, the GA evolutionary version of the compact design improves in the mean rank to the rest of classical coding strategies, and in most cases outperforms the binary compact approach in the present experiment. This result is expected since the evolutionary version looks for a compact ECOC matrix configuration that minimizes the error over the training data. In particular, the advantage of the evolutionary version over the binary one is more significant when the number of classes increases, since more compact matrices are available for optimization.

## 计算机代写|机器学习代写machine learning代考|Labelled Faces in the Wild Categorization

This dataset contains 13000 faces images taken directly from the web from over 1400 people. These images are not constrained in terms of pose, light, occlusions or any other relevant factor. For the purpose of this experiment we used a specific subset, taking only the categories which at least have four or more examples, having a total of 610 face categories. Finally, in order to extract relevant features from the images, we apply an Incremental Principal Component Analysis procedure [16], keeping $99.8 \%$ of the information. An example of face images is shown in Fig. 2.4.
The results in the first row of Table 2.3 show that the best performance is obtained by the Evolutionary GA and PBIL compact strategies. One important observation is that Evolutionary strategies outperform the classical one-versus-all approach, with far less number of classifiers (10 instead of 610). Note that in this case we omitted the one-vs-one strategy since it requires 185745 classifiers for discriminating 610 face categories.

For this second computer vision experiment, we use the video sequences obtained from the Mobile Mapping System of [1] to test the ECOC methodology on a real traffic sign categorization problem. In this system, the position and orientation of the different traffic signs are measured with video cameras fixed on a moving vehicle. The system has a stereo pair of calibrated cameras, which are synchronized with a GPS/INS system. The result of the acquisition step is a set of stereo-pairs of images with their position and orientation information. From this system, a set of 36 circular and triangular traffic sign classes are obtained. Some categories from this data set are shown in Fig. 2.5. The data set contains a total of 3481 samples of size $32 \times 32$, filtered using the Weickert anisotropic filter, masked to exclude the background pixels, and equalized to prevent the effects of illumination changes. These feature vectors are then projected into a 100 feature vector by means of PCA.

The classification results obtained when considering the different ECOC configurations are shown in the second row of Table 2.3. The ECOC designs obtain similar classification results with an accuracy of over $90 \%$. However, note that the compact methodologies use six times less classifiers than the one-versus-all and 105 less times classifiers than the one-versus-one approach, respectively.

# 机器学习代考

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

$$X_F^2=\frac{12 N}{k(k+1)}\left[\sum_j R_j^2-\frac{k(k+1)^2}{4}\right] .$$

$$F_F=\frac{(N-1) X_F^2}{N(k-1)-X_F^2}$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|QBUS3820

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

## 计算机代写|机器学习代写machine learning代考|Evolutionary Compact Parametrization

When defining a compact design of an ECOC, the possible loss of generalization performance has to be taken into account. In order to deal with this problem an evolutionary optimization process is used to find a compact ECOC with high generalization capability.

In order to show the parametrization complexity of the compact ECOC design, we first provide an estimation of the number of different possible ECOC matrices that we can build, and therefore, the search space cardinality. We approximate this number using some simple combinatorial principles. First of all, if we have an $N$-class problem and $B$ possible bits to represent all the classes, we have a set $C W$ with $2^B$ different words. In order to build an ECOC matrix, we select $N$ codewords from $C W$ without replacement. In combinatorics this is represented as $\left(\begin{array}{c}2_N^B \ N\end{array}\right)$, which means that we can construct $V_{2^B}^N=\frac{2^{B} !}{\left(2^B-N\right) !}$ different ECOC matrices. Nevertheless, in the ECOC framework, one matrix and its opposite (swapping all zeros by ones and vice-versa) are considered as the same matrix, since both represent the same partitions of the data. Therefore, the approximated number of possible ECOC matrices with the minimum number of classifiers is $\frac{V_{2 B}^N}{2}=\frac{2^{B} !}{2\left(2^B-N\right) !}$. In addition to the huge cardinality, it is easy to show that this space is neither continuous nor differentiable, because a change in just one bit of the matrix may produce a wrong coding design.

In this type of scenarios, evolutionary approaches are often introduced with good results. Evolutionary algorithms are a wide family of methods that are inspired on the Darwin’s evolution theory, and used to be formulated as optimization processes where the solution space is neither differentiable nor well defined. In these cases, the simulation of natural evolution process using computers results in stochastic optimization techniques which often outperform classical methods of optimization when applied to difficult real-world problems. Although the most used and studied evolutionary algorithms are the Genetic Algorithms (GA), from the publication of the Population Based Incremental Learning (PBIL) in 1995 by Baluja and Caruana [4], a new family of evolutionary methods is striving to find a place in this field. In contrast to $\mathrm{GA}$, those new algorithms consider each value in the chromosome as a random variable, and their goal is to learn a probability model to describe the characteristics of good individuals. In the case of PBIL, if a binary chromosome is used, a uniform distribution is learned in order to estimate the probability of each variable to be one or zero.

In this chapter, we report experiments made with the selected evolutionary strategies – i.e. GA and PBIL. Note that for both Evolutionary Strategies, the encoding step and the adaptation function are exactly equivalent.

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

Problem encoding: The first step in order to use an evolutionary algorithm is to define the problem encoding, which consists of the representation of a certain solution or point in the search space by means of a genotype or alternatively a chromosome [14]. When the solutions or individuals are transformed in order to be represented in a chromosome, the original values (the individuals) are referred as phenotypes, and each one of the possible settings for a phenotype is the allele. Binary encoding is the most common, mainly because the first works about GA used this type of encoding. In binary encoding, every chromosome is a string of bits. Although this encoding is often not natural for many problems and sometimes corrections must be performed after crossover and/or mutation, in our case, the chromosomes represent binary ECOC matrices, and therefore, this encoding perfectly adapts to the problem. Each ECOC is encoded as a binary chromosome $\zeta=$, where $h_i^{c_j} \in{0,1}$ is the expected value of the $i$-th classifier for the class $c_j$, which corresponds to the $i-t h$ bit of the class $c_j$ codeword.

Adaptation function: Once the encoding is defined, we need to define the adaptation function, which associates to each individual its adaptation value to the environment, and thus, their survival probability. In the case of the ECOC framework, the adaptation value must be related to the classification error.

Given a chromosome $\zeta=\left\langle\zeta_0, \zeta_1, \ldots, \zeta_L>\right.$ with $\zeta_i \in{0,1}$, the first step is to recover the ECOC matrix $M$ codified in this chromosome. The elements of $M$ allow to create binary classification problems from the original multi-class problem, following the partitions defined by the ECOC columns. Each binary problem is addressed by means of a binary classifier, which is trained in order to separate both partitions of classes. Assuming that there exists a function $y=f(x)$ that maps each sample $x$ to its real label $y$, training a classifier consists of finding the best parameters $w^$ of a certain function $y=f^{\prime}(x, w)$, in the manner that for any other $w \neq w^, f^{\prime}\left(x, w^\right)$ is a better approximation to $f$ than $f^{\prime}(x, w)$. Once the $w^$ are estimated for each binary problem, the adaptation value corresponds to the classification error. In order to take into account the generalization power of the trained classifiers, the estimation of $w^*$ is performed over a subset of the samples, while the rest of the samples are reserved for a validation set, and the adaptation value $\xi$ is the classification error over that validation subset. The adaptation value for an individual represented by a certain chromosome $\zeta_i$ can be formulated as:
$$\varepsilon_i\left(P, Y, M_i\right)=\frac{\sum_{j=1}^s \delta\left(\rho_j, M_i\right) \neq y_j}{s},$$
where $M_i$ is the ECOC matrix encoded in $\zeta_i, P=\left\langle\rho_1, \ldots, \rho_s\right\rangle$ a set of samples, $Y=\left\langle y_1, \ldots, y_s\right\rangle$ the expected labels for samples in $P$, and $\delta$ is the function that returns the classification label applying the decoding strategy.

# 机器学习代考

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

$$\varepsilon_i\left(P, Y, M_i\right)=\frac{\sum_{j=1}^s \delta\left(\rho_j, M_i\right) \neq y_j}{s},$$

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|MKTG6010

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

## 计算机代写|机器学习代写machine learning代考|Local Binary Patterns

The local binary pattern (LBP) operator [14] is a powerful $2 \mathrm{D}$ texture descriptor that has the benefit of being somewhat insensitive to variations in the lighting and orientation of an image. The method has been successfully applied to applications such as face recognition [1] and facial expression recognition [16]. As illustrated in Fig. 1.2, the LBP algorithm associates each interior pixel of an intensity image with a binary code number in the range $0-256$. This code number is generated by taking the surrounding pixels and, working in a clockwise direction from the top left hand corner, assigning a bit value of 0 where the neighbouring pixel intensity is less than that of the central pixel and 1 otherwise. The concatenation of these bits produces an eight-digit binary code word which becomes the grey-scale value of the corresponding pixel in the transformed image. Figure 1.2 shows a pixel being compared with its immediate neighbours. It is however also possible to compare a pixel with others which are separated by distances of two, three or more pixel widths, giving rise to a series of transformed images. Each such image is generated using a different radius for the circularly symmetric neighbourhood over which the LBP code is calculated.

Another possible refinement is to obtain a finer angular resolution by using more than 8 bits in the code-word [14]. Note that the choice of the top left hand corner as a reference point is arbitrary and that different choices would lead to different LBP codes; valid comparisons can be made, however, provided that the same choice of reference point is made for all pixels in all images.

It is noted in [14] that in practice the majority of LBP codes consist of a concatenation of at most three consecutive sub-strings of $0 \mathrm{~s}$ and $1 \mathrm{~s}$; this means that when the circular neighbourhood of the centre pixel is traversed, the result is either all $0 \mathrm{~s}$, all $1 \mathrm{~s}$ or a starting point can be found which produces a sequence of 0 s followed by a sequence of $1 \mathrm{~s}$. These codes are referred to as uniform patterns and, for an 8 bit code, there are 58 possible values. Uniform patterns are most useful for texture discrimination purposes as they represent local micro-features such as bright spots, flat spots and edges; non-uniform patterns tend to be a source of noise and can therefore usefully be mapped to the single common value 59 .

In order to use LBP codes as a face expression comparison mechanism it is first necessary to subdivide a face image into a number of sub-windows and then compute the occurrence histograms of the LBP codes over these regions. These histograms can be combined to generate useful features, for example by concatenating them or by comparing corresponding histograms from two images.

## 计算机代写|机器学习代写machine learning代考|Fast Correlation-Based Filtering

Broadly speaking, feature selection algorithms can be divided into two groups: wrapper methods and filter methods [3]. In the wrapper approach different combinations of features are considered and a classifier is trained on each combination to determine which is the most effective. Whilst this approach undoubtedly gives good results, the computational demands that it imposes render it impractical when a very large number of features needs to be considered. In such cases the filter approach may be used; this considers the merits of features in themselves without reference to any particular classification method.

Fast correlation-based filtering (FCBF) has proved itself to be a successful feature selection method that can handle large numbers of features in a computationally efficient way. It works by considering the classification between each feature and the class label and between each pair of features. As a measure of classification the concept of symmetric uncertainty is used; for a pair random variables $X$ and $Y$ this is defined as:
$$S U(X, Y)=2\left[\frac{I G(X, Y)}{H(X)+H(Y)}\right]$$
where $H(\cdot)$ is the entropy of the random variable and $I G(X, Y)=H(X)-H(X \mid Y)=$ $H(Y)-H(Y \mid X)$ is the information gain between $X$ and $Y$. As its name suggests, symmetric uncertainty is symmetric in its arguments; it takes values in the range $[0,1]$ where 0 implies independence between the random variables and 1 implies that the value of each variable completely predicts the value of the other. In calculating the entropies of Eq. 1.6, any continuous features must first be discretised.

The FCBF algorithm applies heuristic principles that aim to achieve a balance between using relevant features and avoiding redundant features. It does this by selecting features $f$ that satisfy the following properties:

1. $S U(f, c) \geq \delta$ where $c$ is the class label and $\delta$ is a threshold value chosen to suit the application.
2. $\forall g: S U(f, g) \geq S U(f, c) \Rightarrow S U(f, c) \geq S U(g, c)$ where $g$ is any feature other than $f$.

Here, property 1 ensures that the selected features are relevant, in that they are correlated with the class label to some degree, and property 2 eliminates redundant features by discarding those that are strongly correlated with a more relevant feature.

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|Fast Correlation-Based Filtering

$$S U(X, Y)=2\left[\frac{I G(X, Y)}{H(X)+H(Y)}\right]$$

I didn’t have a fallback condition. The regression line, taking in the ambient temperature, tried to compensate for the untested range of data (the viscosity curve wasn’t actually linear at that range), and took a chuck that normally rotated at around 2,800 RPM and tried to instruct it to spin at 15,000 RPM.

I spent the next four days and three nights cleaning up lacquer from the inside of that machine. By the time I was finished, the lead engineer took me aside and handed me a massive three-ring binder and told me to “read it before playing any more games.” (I’m paraphrasing. I can’t put into print what he said to me.) The book was filled with the materials science analysis of each chemical that the machine was using. It had the exact viscosity curves that I could have used. It had information on maximum spin speeds for deposition.

## 计算机代写|机器学习代写machine learning代考|Cold-start woes

For certain types of ML projects, model prediction failures are not only frequent, but also expected. For solutions that require a historical context of existing data to function properly, the absence of historical data prevents the model from making a prediction. The data simply isn’t available to pass through the model. Known as the cold-start problem, this is a critical aspect of solution design and architecture for any project dealing with temporally associated data.

As an example, let’s imagine that we run a dog-grooming business. Our fleets of mobile bathing stations scour the suburbs of North America, offering all manner of services to dogs at their homes. Appointments and service selection is handled through an app interface. When booking a visit, the clients select from hundreds of options and prepay for the services through the app no later than a day before the visit.

To increase our customers’ satisfaction (and increase our revenue), we employ a service recommendation interface on the app. This model queries the customer’s historical visits, finds products that might be relevant for them, and indicates additional services that the dog might enjoy. For this recommender to function correctly, the historical services history needs to be present during service selection.

This isn’t much of a stretch for anyone to conceptualize. A model without data to process isn’t particularly useful. With no history available, the model clearly has no data in which to infer additional services that could be recommended for bundling into the appointment.

What’s needed to serve something to the end user is a cold-start solution. An easy implementation for this use case is to generate a collection of the most frequently ordered services globally. If the model doesn’t have enough data to provide a prediction, this popularity-based services aggregation can be served in its place. At that point, the app IFrame element will at least have something in it (instead of showing an empty collection) and the user experience won’t be broken by seeing an empty box.

# 机器学习代考

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|Clarifying correlation vs. causation

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

## 计算机代写|机器学习代写machine learning代考|Clarifying correlation vs. causation

An important part of presenting model results to a business unit is to be clear about the differences between correlation and causation. If there is even a slight chance of business leaders inferring a causal relationship from anything that you are showing them, it’s best to have this chat.

Correlation is simply the relationship or association that observed variables have to one another. It does not imply any meaning apart from the existence of this relationship. This concept is inherently counterintuitive to laypersons who are not involved in analyzing data. Making reductionist conclusions that “seem to make sense” about the data relationships in an analysis is effectively how our brains are wired.

For example, we could collect sales data for ice cream trucks and sales of mittens, both aggregated by week of year and country. We could calculate a strong negative correlation between the two (ice cream sales go up as mitten sales increase, and vice versa). Most people would chuckle at a conclusion of causality: “Well, if we want to sell more ice cream, we need to reduce our supply of mittens!”

What a layperson might instantly state from such a silly example is, “Well, people buy mittens when it’s cold and ice cream when it’s hot.” This is an attempt at defining causation. Based on this negative correlation in the observed data, we definitely can’t make such an inference regarding causation. We have no way of knowing what actually influenced the effect of purchasing ice cream or mittens on an individual basis (per observation).

If we were to introduce an additional confounding variable to this analysis (outside temperature), we might find additional confirmation of our spurious conclusion. However, this ignores the complexity of what drives decisions to purchase. As an example, see figure 11.7.

It’s clear that a relationship is present. As temperature increases, ice cream sales increase as well. The relationship being exhibited is fairly strong. But can we infer anything other than the fact that there is a relationship?

Let’s look at another plot. Figure 11.8 shows an additional observational data point that we could put into a model to aid in predicting whether someone might want to buy our ice cream.

## 计算机代写|机器学习代写machine learning代考|Leveraging A/B testing for attribution calculations

In the previous section, we established the importance of attribution measurement. For our ice cream coupon model, we defined a methodology to split our customer base into different cohort segments to minimize latent variable influence. We’ve defined why it’s so critical to evaluate the success criteria of our implementation based on business metrics associated with what we’re trying to improve (our revenue).

Armed with this understanding, how do we go about calculating the impact? How can we make an adjudication that is mathematically sound and provides an irrefutable assessment of something as complex as a model’s impact on the business?
A/B testing 101
Now that we have defined our cohorts by using a simple percentile-based RFM segmentation (the three groups that we assigned to customers in section 11.1.1), we’re ready to conduct random stratified sampling of our customers to determine which coupon experience they will get.

The control group will be getting the pre-ML treatment of a generic coupon being sent to their inbox on Mondays at 8 a.m. PST. The test group will be getting the targeted content and delivery timing.
NOTE Although simultaneously releasing multiple elements of a project that are all significant departures from the control conditions may seem counterintuitive for hypothesis testing (and it is confounding to a causal relationship), most companies are (wisely) willing to forego scientific accuracy of evaluations in the interest of getting a solution out into the world as soon as possible. If you’re ever faced with this supposed violation of statistical standards, my best advice is this: keep patiently quiet and realize that you can do variation tests later by changing aspects of the implementation in further A/B tests to determine causal impacts to the different aspects of your solution. When it’s time to release a solution, it’s often much more worthwhile to release the best possible solution first and then analyze components later.
Within a short period after production release, people typically want to see plots illustrating the impact as soon as the data starts rolling in. Many line charts will be created, aggregating business parameter results based on the control and test group. Before letting everyone go hog wild with making fancy charts, a few critical aspects of the hypothesis test need to be defined to make it a successful adjudication.

# 机器学习代考

A/B测试101

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。

## 计算机代写|机器学习代写machine learning代考|Use of global mutable objects

statistics-lab™ 为您的留学生涯保驾护航 在代写机器学习 machine learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

## 计算机代写|机器学习代写machine learning代考|Use of global mutable objects

Continuing our exploration of our new team’s existing code base, we’re tackling another new feature to be added. This one adds completely new functionality. In the process of developing it, we realize that a large portion of the necessary logic for our branch already exists and we simply need to reuse a few methods and a function. What we fail to see is that the function uses a declaration of a globally scoped variable. When running our tests for our branch in isolation (through unit tests), everything works exactly as intended. However, the integration test of the entire code base produces a nonsensical result.

After hours of searching through the code, walking through debugging traces, we find that the state of the function that we were using actually changed from its first usage, and the global variable that the function was using actually changed, rendering our second use of it completely incorrect. We were burned by mutation.
How mutability can burn you
Recognizing how dangerous mutability is can be a bit tricky. Overuse of mutating values, shifting state, and overwriting of data can take many forms, but the end result is typically the same: an incredibly complicated series of bugs. These bugs can manifest themselves in different ways: Heisenbugs seemingly disappear when you’re trying to investigate them, and Mandelbugs are so complex and nondeterministic that they seem to be as complex as a fractal. Refactoring code bases that are riddled with mutation is nontrivial, and many times it’s simply easier to start over from scratch to fix the design flaws.
Issues with mutation and side effects typically don’t rear their heads until long after the initial MVP of a project. Later, in the development process or after a production release, flawed code bases relying on mutability and side effects start to break apart at the seams. Figure 10.3 shows an example of the nuances between different languages and their execution environments and why mutability concerns might not be as apparent, depending on which languages you’re familiar with.

For simplicity’s sake, let’s say that we’re trying to keep track of some fields to include in separate vectors used in an ensemble modeling problem. The following listing shows a simple function that contains a default value within the function signature’s parameters which, when used a single time, will provide the expected functionality.

## 计算机代写|机器学习代写machine learning代考|Encapsulation to prevent mutable side effects

By knowing that the Python functions maintain state (and everything is mutable in this language), we could have anticipated this behavior. Instead of applying a default argument to maintain isolation and break the object-mutation state, we should have initialized this function with a state that could be checked against.

By performing this simple state validation, we are letting the interpreter know that in order to satisfy the logic, a new object needs to be created to store the new list of values. The proper implementation for checking on instance state in Python for collection mutation is shown in the following listing.

Seemingly small issues like this can create endless headaches for the person (or team) implementing a project. Typically, these sorts of problems are developed early on, showing no issues while the modules are being built out. Even simple unit tests that validate this functionality in isolation will appear to be functioning correctly.

It is typically toward the midpoint of an MVP that issues involving mutability begin to rear their ugly heads. As greater complexity is built out, functions and classes may be utilized multiple times (which is a desired pattern in development), and if not implemented properly, what was seeming to work just fine before now results in difficult-to-troubleshoot bugs.
PRO TIP It’s best to become familiar with the way your development language handles objects, primitives, and collections. Knowing these core nuances of the language will give you the tools necessary to guide your development in a way that won’t create more work and frustration for you throughout the process.
A note on encapsulation
Throughout this book, you’ll see multiple references to me beating a dead horse about using functions in favor of declarative code. You’ll also notice references to favoring classes and methods to functions. This is all due to the overwhelming benefits that come with using encapsulation (and abstraction, but that’s another story discussed elsewhere in the text).
Encapsulating code has two primary benefits:

Enforcing execution of logic on a bundle of the data being passed in and the logic contained within the method

# 机器学习代考

## 有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## 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。