### 机器学习代写|决策树作业代写decision tree代考|Classification

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• 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

## 机器学习代写|决策树作业代写decision tree代考|Introduction

Classification, which is the data mining task of assigning objects to predefined categories, is widely used in the process of intelligent decision making. Many classification techniques have been proposed by researchers in machine learning, statistics, and pattern recognition. Such techniques can be roughly divided according to the their level of comprehensibility. For instance, techniques that produce interpretable classification models are known as white-box approaches, whereas those that do not are known as black-box approaches. There are several advantages in employing white-box techniques for classification, such as increasing the user confidence in the prediction, providing new insight about the classification problem, and allowing the detection of errors either in the model or in the data [12]. Examples of white-box classification techniques are classification rules and decision trees. The latter is the main focus of this book.

A decision tree is a classifier represented by a flowchart-like tree structure that has been widely used to represent classification models, specially due to its comprehensible nature that resembles the human reasoning. In a recent poll from the kdnuggets website [13], decision trees figured as the most used data mining/analytic method by researchers and practitioners, reaffirming its importance in machine learning tasks. Decision-tree induction algorithms present several advantages over other learning algorithms, such as robustness to noise, low computational cost for generating the model, and ability to deal with redundant attributes [22].

Several attempts on optimising decision-tree algorithms have been made by researchers within the last decades, even though the most successful algorithms date back to the mid-80s [4] and early $90 \mathrm{~s}[21]$. Many strategies were employed for deriving accurate decision trees, such as bottom-up induction $[1,17]$, linear programming [3], hybrid induction [15], and ensemble of trees [5], just to name a few. Nevertheless, no strategy has been more successful in generating accurate and comprehensible decision trees with low computational effort than the greedy top-down induction strategy.

A greedy top-down decision-tree induction algorithm recursively analyses if a sample of data should be partitioned into subsets according to a given rule, or if no further partitioning is needed. This analysis takes into account a stopping criterion, for

deciding when tree growth should halt, and a splitting criterion, which is responsible for choosing the “best” rule for partitioning a subset. Further improvements over this basic strategy include pruning tree nodes for enhancing the tree’s capability of dealing with noisy data, and strategies for dealing with missing values, imbalanced classes, oblique splits, among others.

A very large number of approaches were proposed in the literature for each one of these design components of decision-tree induction algorithms. For instance, new measures for node-splitting tailored to a vast number of application domains were proposed, as well as many different strategies for selecting multiple attributes for composing the node rule (multivariate split). There are even studies in the literature that survey the numerous approaches for pruning a decision tree $[6,9]$. It is clear that by improving these design components, more effective decision-tree induction algorithms can be obtained.

## 机器学习代写|决策树作业代写decision tree代考|Book Outline

This book is structured in 7 chapters, as follows.
Chapter 2 [Decision-Tree Induction]. This chapter presents the origins, basic concepts, detailed components of top-down induction, and also other decision-tree induction strategies.

Chapter 3 [Evolutionary Algorithms and Hyper-Heuristics]. This chapter covers the origins, basic concepts, and techniques for both Evolutionary Algorithms and Hyper-Heuristics.

Chapter 4 [HEAD-DT: Automatic Design of Decision-Tree Induction Algorithms]. This chapter introduces and discusses the hyper-heuristic evolutionary algorithm that is capable of automatically designing decision-tree algorithms. Details such as the evolutionary scheme, building blocks, fitness evaluation, selection, genetic operators, and search space are covered in depth.

Chapter 5 [HEAD-DT: Experimental Analysis]. This chapter presents a thorough empirical analysis on the distinct scenarios in which HEAD-DT may be applied to. In addition, a discussion on the cost effectiveness of automatic design, as well as examples of automatically-designed algorithms and a baseline comparison between genetic and random search are also presented.

Chapter 6 [HEAD-DT: Fitness Function Analysis]. This chapter conducts an investigation of 15 distinct versions for HEAD-DT by varying its fitness function, and a new set of experiments with the best-performing strategies in balanced and imbalanced data sets is described.

Chapter 7 [Conclusions]. We finish this book by presenting the current limitations of the automatic design, as well as our view of several exciting opportunities for future work.

## 机器学习代写|决策树作业代写decision tree代考|Decision-tree induction algorithms

Abstract Decision-tree induction algorithms are highly used in a variety of domains for knowledge discovery and pattern recognition. They have the advantage of producing a comprehensible classification/regression model and satisfactory accuracy levels in several application domains, such as medical diagnosis and credit risk assessment. In this chapter, we present in detail the most common approach for decision-tree induction: top-down induction (Sect. 2.3). Furthermore, we briefly comment on some alternative strategies for induction of decision trees (Sect. 2.4). Our goal is to summarize the main design options one has to face when building decision-tree induction algorithms. These design choices will be specially interesting when designing an evolutionary algorithm for evolving decision-tree induction algorithms.

Keywords Decision trees – Hunt’s algorithm . Top-down induction – Design components

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