在Latex中的\textwidth内安装表格

我有一个比文档的\text宽度更宽的表。（我不想更改\text宽度。） 我想通过包装最后一列（最右边的一列）的文本，强制将表格装入\textwidth。 这是我表格的代码：

\begin{table*}[!t]
\caption{Extracted Features used in Meta-Learning} \label{sample-features}
\label{tbl:features}
\begin{center}
\begin{tabular*}{0.95\textwidth,center}{|l||l|}
{\textbf{FEATURE}}  &{\textbf{DESCRIPTION}} \\
\hline \\
n.examples & Number of examples \\
n.attrs & Number of attributes \\
prop.symbolic.attrs & Proportion of symbolic attributes \\
prop.missing.values & Proportion of missing values \\
class.entropy & Class entropy \\
avg.mutual.information & Average mutual information \\
prop.h.outlier & Proportion of continuous attributes with outliers \\
avg.attr.entropy & Average attribute entropy \\
avg.symb.pair.mutual.infor & Average mutual information between pairs of symbolic attributes \\
avg.abs.attr.correlation & Average absolute correlation between continuous attributes \\
avg.skewness & Mean skewness of attributes \\
avg.abs.skewness & Mean absolute skewness of attributes \\
avg.kurtosis & Mean kurtosis of attributes \\
canonical.correlation.best.linear.combination & Canonical correlation of the best linear combination of attributes to distinguish between classes \\
relative.prop.best.linear.combination & Proportion of the total discrimination power explained by the best linear combination \\
\end{tabular*}
\end{center}
\end{table*}

此代码的结果是以下文档：

红线右侧的部分（我自己加的）应该包起来

---

有什么帮助吗？

这里是答案。我希望它能帮助您：

使用包

表格

，如下所示：

\usepackage{tabularx,booktabs}
.
.
.
\begin{table*}[!t]
\caption{Caption}
\label{tbl:tbl}
\centering
\begin{tabularx}{\textwidth}{X X}
\toprule
Item 1 & Item 2 \\
\midrule
Item 1 description & Item 2 description \\
.
.
.
\bottomrule
\end{tabularx}
\end{table*}

\begin{table*}[!t]
\caption{Extracted Features used in Meta-Learning} \label{sample-features}
\label{tbl:features}
\centering
\begin{tabularx}{0.95\textwidth}{|X||X|}
\toprule
{\textbf{FEATURE}}  &{\textbf{DESCRIPTION}} \\
\midrule
n.examples & Number of examples \\
n.attrs & Number of attributes \\
prop.symbolic.attrs & Proportion of symbolic attributes \\
prop.missing.values & Proportion of missing values \\
class.entropy & Class entropy \\
avg.mutual.information & Average mutual information \\
prop.h.outlier & Proportion of continuous attributes with outliers \\
avg.attr.entropy & Average attribute entropy \\
avg.symb.pair.mutual.infor & Average mutual information between pairs of symbolic attributes \\
avg.abs.attr.correlation & Average absolute correlation between continuous attributes \\
avg.skewness & Mean skewness of attributes \\
avg.abs.skewness & Mean absolute skewness of attributes \\
avg.kurtosis & Mean kurtosis of attributes \\
canonical.correlation.best.linear.combination & Canonical correlation of the best linear combination of attributes to distinguish between classes \\
relative.prop.best.linear.combination & Proportion of the total discrimination power explained by the best linear combination \\
\bottomrule
\end{tabularx}
\end{table*}

在我的情况下，解决方案如下：

\usepackage{tabularx,booktabs}
.
.
.
\begin{table*}[!t]
\caption{Caption}
\label{tbl:tbl}
\centering
\begin{tabularx}{\textwidth}{X X}
\toprule
Item 1 & Item 2 \\
\midrule
Item 1 description & Item 2 description \\
.
.
.
\bottomrule
\end{tabularx}
\end{table*}

\begin{table*}[!t]
\caption{Extracted Features used in Meta-Learning} \label{sample-features}
\label{tbl:features}
\centering
\begin{tabularx}{0.95\textwidth}{|X||X|}
\toprule
{\textbf{FEATURE}}  &{\textbf{DESCRIPTION}} \\
\midrule
n.examples & Number of examples \\
n.attrs & Number of attributes \\
prop.symbolic.attrs & Proportion of symbolic attributes \\
prop.missing.values & Proportion of missing values \\
class.entropy & Class entropy \\
avg.mutual.information & Average mutual information \\
prop.h.outlier & Proportion of continuous attributes with outliers \\
avg.attr.entropy & Average attribute entropy \\
avg.symb.pair.mutual.infor & Average mutual information between pairs of symbolic attributes \\
avg.abs.attr.correlation & Average absolute correlation between continuous attributes \\
avg.skewness & Mean skewness of attributes \\
avg.abs.skewness & Mean absolute skewness of attributes \\
avg.kurtosis & Mean kurtosis of attributes \\
canonical.correlation.best.linear.combination & Canonical correlation of the best linear combination of attributes to distinguish between classes \\
relative.prop.best.linear.combination & Proportion of the total discrimination power explained by the best linear combination \\
\bottomrule
\end{tabularx}
\end{table*}

你好您是否尝试过一些段落对齐方式，如

lp{.6\textwidth}

而不是

X

？另外：