class_docstr: Analyze results of machine learning experiments.
docstr: Box plot of metric.
docstr_tok: ["Box","plot","of","metric","."]
code: def plot_boxplots(self, metric_name=None, **kwargs):
      """Box plot of metric."""
      _check_soft_dependencies("matplotlib")
  
      import matplotlib.pyplot as plt  # noqa: E402
  
      plt.style.use("seaborn-ticks")
  
      self._check_is_evaluated()
      metric_name = self._validate_metric_name(metric_name)
      column = self._get_column_name(metric_name, suffix="mean")
  
      fig, ax = plt.subplots(1)
      self.metrics_by_strategy_dataset.boxplot(
          by="strategy", column=column, grid=False, ax=ax, **kwargs
      )
      ax.set(
          title=f"{metric_name} by strategy",
          xlabel="strategies",
          ylabel=metric_name,
      )
      fig.suptitle(None)
      plt.tight_layout()
      return fig, ax
  
imports: import itertools
  import numpy
  import pandas
  from scipy import stats
  from scipy.stats import ranksums, ttest_ind
  from sktime.benchmarking.base import BaseResults
  from sktime.exceptions import NotEvaluatedError
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 7 for calling the Evaluator obj's underlying member method code with example usage: obj.plot_boxplots(metric_name) and returns: fig,  ax