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LaTeX 算法环境和 \mathbb{ } 错误

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LaTeX 算法环境和 \mathbb{ } 错误

我正在完成我的论文。然而，论文中的最后一个障碍是算法环境中的整数 Z 符号。当我使用\mathbb{Z}它时，它会返回错误：! LaTeX Error: \mathbb allowed only in math mode.

以下是算法示例：

\begin{algorithm}
\begin{algorithmic}[1]
    \For{$b\gets 1, B$}

    \State (a) Draw a bootstrap sample \mathbb{Z}$^*$ of size $N$ from the training data.
    \State (b) Grow a random forest tree $T_b$ to the bootstrapped data, by recursively repeating the following steps for each terminal node of
the tree, until the minimum node size $n_{min}$ is reached.
    \State\indent (I) \ $\>$  Select $m$ variables at random from the $p$ variables.
    \State\indent (II) $\>$ Pick the best variable/split-point among the $m$.
    \State\indent (III)$\>$  Split the node into two daughter nodes.
    \EndFor

    \State Output the ensemble of trees $\{ T_b\}_1^B$

    \State Make prediction at new point $x$:
    \State Let $\hat{C}_b(x)$ be the class prediction be the class prediction of the $b$th random forest tree. Then $\hat{C}_rf^B(x) = \text{ majority vote }\{ \hat{C}_b(x)_1^B\}$.  

\end{algorithmic}
\caption{Random Forest for Classification (RFC)  \protect\cite{friedman2001elements}}\label{alg:randomforest}
\end{algorithm}

答案1

因此，将其置于数学模式中：$\mathbb{Z}^*$

在此处输入图片描述

\documentclass{article}

\usepackage{algorithm,algpseudocode}
\usepackage{amsmath,amsfonts}

\begin{document}

\begin{algorithm}
\begin{algorithmic}[1]
    \For{$b\gets 1, B$}
      \State (a) Draw a bootstrap sample $\mathbb{Z}^*$ of size $N$ from the training data.
      \State (b) Grow a random forest tree $T_b$ to the bootstrapped data, by recursively repeating the following steps for each terminal node of
  the tree, until the minimum node size $n_{\mathrm{min}}$ is reached.
      \State \indent \makebox[2em][l]{(I)  } Select $m$ variables at random from the $p$ variables.
      \State \indent \makebox[2em][l]{(II) } Pick the best variable/split-point among the $m$.
      \State \indent \makebox[2em][l]{(III)} Split the node into two daughter nodes.
    \EndFor

    \State Output the ensemble of trees $\{T_b\}_1^B$

    \State Make prediction at new point $x$:
    \State Let $\hat{C}_b(x)$ be the class prediction be the class prediction of the $b$th random forest tree. Then $\hat{C}_r f^B(x) = \text{majority vote } \{ \hat{C}_b(x)_1^B \}$.  
  \end{algorithmic}
  \caption{Random Forest for Classification (RFC)}
\end{algorithm}

\end{document}

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