初学者指南：2017版机器学习入门-理解与应对挑战

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《机器学习入门指南：绝对初学者版，第二版》（作者：奥利弗·托拜德，2017年）是一本面向零基础读者的机器学习教材，它旨在帮助读者理解并掌握这一领域的基础知识。本书在工业革命后的技术飞跃背景下展开，强调了机器能力的扩展，从传统的工厂作业延伸到包括歌曲比赛评判、自动驾驶和专业国际象棋对弈等原本由人类专有的认知任务。作者在书中首先介绍了什么是机器学习，将机器学习分为几个核心类别，如回归分析、聚类、偏见与方差、人工神经网络、决策树以及集成学习方法等，让读者逐步了解其基本原理。作者通过实际操作，指导读者如何进行数据预处理，设置和组织数据集，以便进行模型构建。在数据分析阶段，作者着重讲解了如何进行数据清洗，确保数据质量对于模型训练的重要性。接下来的部分深入探讨了常见的机器学习算法，如线性回归、K-means聚类算法，以及如何解决过拟合和欠拟合问题，即处理模型的偏差和方差平衡。人工神经网络是书中另一个重点，它模仿人脑神经元的工作方式，是深度学习的基础。决策树作为可解释性强的模型，也被介绍，让读者理解模型如何基于特征做出决策。而集成学习，如随机森林和梯度提升树，通过结合多个模型来提高预测性能，是现代机器学习中的重要策略。在Python环境中，作者详细指导读者如何编写代码实现这些模型，并介绍了模型优化的重要性，如选择合适的损失函数和超参数调整。然而，书中也提醒读者，尽管AI技术和自动化的发展速度惊人，但全面采纳和广泛应用仍面临许多挑战。关于工作自动化和未来机器智能的讨论，作者持谨慎态度，指出虽然某些职业的确存在被机器人取代的风险，但过度恐慌可能源于对技术进展的误解。作者鼓励读者关注当前的研究动态，并保持对未知的批判思考。《机器学习对于绝对初学者，第二版》提供了一个循序渐进的学习路径，使读者不仅能够掌握机器学习的基本概念和技术，还能理解其在现实世界中的应用和潜在影响。同时，它也为那些担心技术变革对就业市场的影响的人们提供了平衡的观点和思考。

Figure 1: Car value prediction model

After the machine deciphers the rules and patterns of the data, it creates what

is known as a model: an algorithmic equation for producing an outcome with

new data based on the rules derived from the training data. Once the model is

prepared, it can be applied to new data and tested for accuracy. After the

model has passed both the training and test data stages, it is ready to be

applied and used in the real world.

In Chapter 13, we will create a model for predicting house values where y is

the actual house price and X are the variables that impact y, such as land size,

location, and the number of rooms. Through supervised learning, we will

create a rule to predict y (house value) based on the given values of various

variables (X).

Examples of supervised learning algorithms include regression analysis,

decision trees, k-nearest neighbors, neural networks, and support vector

machines. Each of these techniques will be introduced later in the book.

Unsupervised Learning

In the case of unsupervised learning, not all variables and data patterns are

classified. Instead, the machine must uncover hidden patterns and create

labels through the use of unsupervised learning algorithms. The k-means

clustering algorithm is a popular example of unsupervised learning. This

simple algorithm groups data points that are found to possess similar features

as shown in Figure 1.

online activities, including spam, fake reviews, fake app installs, and

fraudulent transactions. Whereas traditional fraud protection services draw on

supervised learning models and rule engines, DataVisor uses unsupervised

learning which enables them to detect unclassified categories of attacks in

their early stages.

On their website, DataVisor explains that "to detect attacks, existing solutions

rely on human experience to create rules or labeled training data to tune

models. This means they are unable to detect new attacks that haven’t already

been identified by humans or labeled in training data."

[5]

This means that traditional solutions analyze the chain of activity for a

particular attack and then create rules to predict a repeat attack. Under this

scenario, the dependent variable (y) is the event of an attack and the

independent variables (X) are the common predictor variables of an attack.

Examples of independent variables could be:

a) A sudden large order from an unknown user. I.E. established customers

generally spend less than $100 per order, but a new user spends $8,000 in one

order immediately upon registering their account.

b) A sudden surge of user ratings. I.E. As a typical author and bookseller

on Amazon.com, it’s uncommon for my first published work to receive more

than one book review within the space of one to two days. In general,

approximately 1 in 200 Amazon readers leave a book review and most books

go weeks or months without a review. However, I commonly see competitors

in this category (data science) attracting 20-50 reviews in one day!

(Unsurprisingly, I also see Amazon removing these suspicious reviews weeks

or months later.)

c) Identical or similar user reviews from different users. Following the

same Amazon analogy, I often see user reviews of my book appear on other

books several months later (sometimes with a reference to my name as the

author still included in the review!). Again, Amazon eventually removes

these fake reviews and suspends these accounts for breaking their terms of

service.

d) Suspicious shipping address. I.E. For small businesses that routinely ship

products to local customers, an order from a distant location (where they

don't advertise their products) can in rare cases be an indicator of fraudulent

or malicious activity.

Standalone activities such as a sudden large order or a distant shipping

address may prove too little information to predict sophisticated

cybercriminal activity and more likely to lead to many false positives. But a

model that monitors combinations of independent variables, such as a sudden

large purchase order from the other side of the globe or a landslide of book

reviews that reuse existing content will generally lead to more accurate

predictions. A supervised learning-based model could deconstruct and

classify what these common independent variables are and design a detection

system to identify and prevent repeat offenses.

Sophisticated cybercriminals, though, learn to evade classification-based rule

engines by modifying their tactics. In addition, leading up to an attack,

attackers often register and operate single or multiple accounts and incubate

these accounts with activities that mimic legitimate users. They then utilize

their established account history to evade detection systems, which are

trigger-heavy against recently registered accounts. Supervised learning-based

solutions struggle to detect sleeper cells until the actual damage has been

made and especially with regard to new categories of attacks.

DataVisor and other anti-fraud solution providers therefore leverage

unsupervised learning to address the limitations of supervised learning by

analyzing patterns across hundreds of millions of accounts and identifying

suspicious connections between users—without knowing the actual category

of future attacks. By grouping malicious actors and analyzing their

connections to other accounts, they are able to prevent new types of attacks

whose independent variables are still unlabeled and unclassified. Sleeper cells

in their incubation stage (mimicking legitimate users) are also identified

through their association to malicious accounts. Clustering algorithms such as

k-means clustering can generate these groupings without a full training

dataset in the form of independent variables that clearly label indications of

an attack, such as the four examples listed earlier. Knowledge of the

dependent variable (known attackers) is generally the key to identifying other

attackers before the next attack occurs. The other plus side of unsupervised

learning is companies like DataVisor can uncover entire criminal rings by

identifying subtle correlations across users.

We will cover unsupervised learning later in this book specific to clustering

analysis. Other examples of unsupervised learning include association

analysis, social network analysis, and descending dimension algorithms.

Reinforcement Learning

Reinforcement learning is the third and most advanced algorithm category in

machine learning. Unlike supervised and unsupervised learning,

reinforcement learning continuously improves its model by leveraging

feedback from previous iterations. This is different to supervised and

unsupervised learning, which both reach an indefinite endpoint after a model

is formulated from the training and test data segments.

Reinforcement learning can be complicated and is probably best explained

through an analogy to a video game. As a player progresses through the

virtual space of a game, they learn the value of various actions under different

conditions and become more familiar with the field of play. Those learned

values then inform and influence a player’s subsequent behavior and their

performance immediately improves based on their learning and past

experience.

Reinforcement learning is very similar, where algorithms are set to train the

model through continuous learning. A standard reinforcement learning model

has measurable performance criteria where outputs are not tagged—instead,

they are graded. In the case of self-driving vehicles, avoiding a crash will

allocate a positive score and in the case of chess, avoiding defeat will

likewise receive a positive score.

A specific algorithmic example of reinforcement learning is Q-learning. In Q-

learning, you start with a set environment of states, represented by the

symbol ‘S’. In the game Pac-Man, states could be the challenges, obstacles or

pathways that exist in the game. There may exist a wall to the left, a ghost to

the right, and a power pill above—each representing different states.

The set of possible actions to respond to these states is referred to as “A.” In

the case of Pac-Man, actions are limited to left, right, up, and down

movements, as well as multiple combinations thereof.

The third important symbol is “Q.” Q is the starting value and has an initial

value of “0.”

As Pac-Man explores the space inside the game, two main things will

happen:

1) Q drops as negative things occur after a given state/action

2) Q increases as positive things occur after a given state/action

In Q-learning, the machine will learn to match the action for a given state that

generates or maintains the highest level of Q. It will learn initially through the

process of random movements (actions) under different conditions (states).

The machine will record its results (rewards and penalties) and how they

impact its Q level and store those values to inform and optimize its future

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