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# 机器学习代考_Machine Learning代考_COMP7703 Finding Similar Theories

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## 机器学习代考_Machine Learning代考_ Finding Similar Theories

Selecting pairs of theories to merge requires the definition of a pairwise similarity function on logical explanation theories. To this aim, the similarity of two theories, E1, E2 is defined as the Jaccard similarity of their coverage on a given instance set $X$.
$$\text { similarity }_X\left(E_i, E_j\right)=\frac{\mid \text { coverage }\left(E_i, X\right) \cap \text { coverage }\left(E_j, X\right) \mid}{\mid \text { coverage }\left(E_i, X\right) \cup \operatorname{coverage}\left(E_j, X\right) \mid} \text {. }$$
The larger is the shared coverage of $E_i$ and $E_j$ on $X$, the more similar the two logical explanation theories are. Coverage similarity is a two-faceted measure that captures the premise similarity and the coverage similarity. The former is straightforward: rules with similar premises have similar coverage. The latter balances the premise similarity to avoid rules with similar premises, but low coverage sway the similarity score.
The merge function allows GLocalX to generalize a set of explanation theories while balancing fidelity and complexity through approximate logical entailment. The merge involves two operators-join and cut-to simultaneously generalize and preserve a high level of fidelity. In the logical domain, generalization seldom involves premises relaxation or outright removal. Thus, GLocalX advances the state-of-the-art in exploiting this kind of generalization. Pushing for generalization may pull down fidelity. These contrasting behaviors are the focal point in a local to global setting and must be dealt with accordingly. This problem is tackled with a merge function that handles both rule generalization and fidelity. Suppose there are two explanation theories, $\mathrm{E} 1=\left{e_1, e_2\right}$ and $\mathrm{E} 2=\left{e_1^{\prime}, e_2^{\prime}\right}$, with $e_1, e_1^{\prime}$ explaining a record $\mathrm{x} 1$ and $e_2, e_2^{\prime}$ explaining a record x2.
\begin{aligned} E_1=\left{e_1\right.&={\text { age } \geq 25, \text { job }=\text { unemployed, amount } \leq 10 k} \rightarrow \text { deny } \ e_2 &={\text { age } \geq 50, \text { job }=\text { office clerk }} \rightarrow \text { deny }} \ E_2=\left{e_1^{\prime}\right.&={\text { age } \geq 20, \text { job }=\text { manager, amount }>8 k} \rightarrow \text { accept } \ e_2^{\prime} &={\text { age } \geq 40, \text { job }=\text { office clerk, amount }>5 k} \rightarrow \text { deny }} \end{aligned}

## 机器学习代考_Machine Learning代考_Code

Let’s consider the following data set, which is the data of customer churns for a telecom company. The variables present in the data are state of the customer address, account length in no of days, area code, phone number, whether an international plan or not, voice mail plan, number of vmail messages, total day minutes, total day calls, total day charge, total eve calls, total eve charge, total night minutes, total night calls, total night charge, total intl minutes, total intl calls, total intl charge, customer service calls. The target variable is churn which contains True and False values. The True value is for customers who churned or left the services of the telecom company. The False value is for customers who have an active plan.
Let’s look at the distribution of the target variable.
class_name = ‘churn’ #Target Variable Name
data[class_name $]=$ data[class_name].replace $({$ True: 1, False: 0$})$ data. shape
(3333, 21)
data.drop(‘phone number’, axis=1, inplace=True)
data[class_name].value_counts()
$0 \quad 2850$
1483
Name: churn, dtype: int64

## 机器学习代考机器学习代考寻找相似理论

$$\text { similarity }_X\left(E_i, E_j\right)=\frac{\mid \text { coverage }left(E_i, X\right) \cap \text { coverage }left(E_j, X\right) \mid}{mid \text { coverage }left(E_i, X\right) \cup \operatorname{coverage }left(E_j, X\right) \mid｝ \覆盖率 {. }$$
$E_i$和$E_j$在$X$上的共享覆盖率越大，这两个逻辑解释理论就越相似。覆盖率相似性是一个两方面的衡量标准，它涵盖了前提相似性和覆盖率相似性。前者是直接的：具有相似前提的规则具有相似的覆盖率。后者平衡了前提相似性，以避免具有相似前提的规则，但低覆盖率动摇了相似性得分。

1483

## MATLAB代写

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