### 统计代写|机器学习作业代写Machine Learning代考| Generative and discriminative models

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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|机器学习作业代写Machine Learning代考|Generative and discriminative models

Predictive algorithms are often categorized into generative and discriminative models. This distinction presumes a probabilistic perspective on machine learning algorithms. Generally speaking, a generative model learns the joint probability distribution $p(x, y)$, whereas a discriminative model learns the conditional probability distribution $p(y \mid x)$, in other words, the probability of $y$ given $x$. A generative algorithm models how the data was generated in order to categorize a signal. It is called generative since sampling can generate synthetic data points. Generative models ask the question: Based on my generation assumptions, which category is most likely to generate this signal? Generative models include naïve Bayes, Bayesian networks, Hidden Markov models (HMM) and Markov random fields (MRF).

A discriminative algorithm does not care about how the data was generated, it simply categorizes a given signal. It directly estimates posterior probabilities, they do not attempt to model the underlying probability distributions. Logistic regression, support vector machines (SVM), traditional neural networks and nearest neighbor models fall into this category. Discriminative models tend to perform better since they solve the problem directly, whereas generative models require an intermediate step. However, generative models tend to converge a lot faster.

## 统计代写|机器学习作业代写Machine Learning代考|Evaluation of learner

In order to evaluate the quality of a trained model, we need to evaluate how well the predictions match the observed data. In other words, we need to measure the quality of the fit. The quality is measured using a loss function or objective function. The goal of all loss functions is to measure how well an algorithm is doing against a given data set.

The loss function quantifies the extend to which the model fits the data [14], in other words, it measures the goodness of fit. During training, the machine learning algorithm tries to minimize the loss function, a process called loss minimization. We are minimizing the training loss, or training error, which is the average loss over all the training samples. A loss function calculates the price, the loss, paid for inaccuracies in a classification problem. It is, thus, also called the cost function.

There are many different loss functions. We have already seen the mean absolute error in equation 2.8. Another popular loss function is the mean squared error (MSE) since it is easy to understand and implement. The mean squared error simply takes the difference between the predictions and the ground truth, squares it, and averages it out across the whole data set. The mean squared error is always positive and the closer to zero the better. The mean squared error is computed as shown in equation $2.9$.
$$M S E=\frac{1}{n} \sum_{i=1}^{n}\left(y_{i}-f\left(x_{i}\right)\right)^{2}$$

where
$$\begin{array}{ll} y & =\text { Vector of n predictions } \ n & =\text { Number of predictions } \ f\left(x_{i}\right) & =\text { Prediction for the } i \text { th observation } \end{array}$$

## 统计代写|机器学习作业代写Machine Learning代考| Stochastic gradient descent

During training we try to minimize a loss function. So we need to make sure we learn into the right “direction” and not getting worse. For a simple loss function, we could use calculus to find the optimal parameters that minimize the loss function. However, for a more complex loss function, calculus might not be feasible anymore. One approach is to iteratively optimize the objective function by using the gradient of the function. This iterative optimization process is called gradient descent where the gradient tells us which direction to move to decrease the loss function. Figure $2.9$ shows gradient descent with a hypothetical objective function $w^{2}$ and the derivative $2 w$ that gives us the slope. If the derivative is positive, it slopes downwards to the left as shown in Figure $2.9$, if it is negative, it slopes to the right. The value of the derivative is multiplied by the constant step size $\eta$, called the learning rate, and subtracted from the current value. Once the change of the parameter value becomes too small, in Figure $2.9$ when it approaches zero, the process stops. The hyperparameter $\eta$ is the step size, which defines how fast we move to the minimum.

The problem with gradient descent is that it is slow since every iteration has to go through all the training instances, the batch, which is expensive, especially if the training set is large. The training loss is the sum over all the training data, which means, the algorithm has to go through all training instances to move one step down.

## 广义线性模型代考

statistics-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。