[docs] [python] docstrings improving (#894)

* fixed description of Dataset

* fixed description of Booster

* fixed description of train

* fixed description of cv

* fixed description of callbacks

* fixed description of plotting

* hotfixes

python-package/lightgbm/callback.py

@@ -10,6 +10,7 @@ from .compat import range_
 
 class EarlyStopException(Exception):
     """Exception of early stopping.
+
     Parameters
     ----------
     best_iteration : int
@@ -46,20 +47,19 @@ def _format_eval_result(value, show_stdv=True):
 
 
 def print_evaluation(period=1, show_stdv=True):
-    """Create a callback that print evaluation result.
+    """Create a callback that prints the evaluation results.
 
     Parameters
     ----------
-    period : int
-        The period to log the evaluation results
-
-    show_stdv : bool, optional
-        Whether show stdv if provided
+    period : int, optional (default=1)
+        The period to print the evaluation results.
+    show_stdv : bool, optional (default=True)
+        Whether to show stdv (if provided).
 
     Returns
     -------
     callback : function
-        A callback that print evaluation every period iterations.
+        The callback that prints the evaluation results every ``period`` iteration(s).
     """
     def callback(env):
         """internal function"""
@@ -71,7 +71,7 @@ def print_evaluation(period=1, show_stdv=True):
 
 
 def record_evaluation(eval_result):
-    """Create a call back that records the evaluation history into eval_result.
+    """Create a callback that records the evaluation history into ``eval_result``.
 
     Parameters
     ----------
@@ -81,7 +81,7 @@ def record_evaluation(eval_result):
     Returns
     -------
     callback : function
-        The requested callback function.
+        The callback that records the evaluation history into the passed dictionary.
     """
     if not isinstance(eval_result, dict):
         raise TypeError('Eval_result should be a dictionary')
@@ -103,22 +103,25 @@ def record_evaluation(eval_result):
 
 
 def reset_parameter(**kwargs):
-    """Reset parameter after first iteration
+    """Create a callback that resets the parameter after the first iteration.
 
-    NOTE: the initial parameter will still take in-effect on first iteration.
+    Note
+    ----
+    The initial parameter will still take in-effect on first iteration.
 
     Parameters
     ----------
     **kwargs: value should be list or function
         List of parameters for each boosting round
-        or a customized function that calculates learning_rate in terms of
-        current number of round (e.g. yields learning rate decay)
-        - list l: parameter = l[current_round]
-        - function f: parameter = f(current_round)
+        or a customized function that calculates the parameter in terms of
+        current number of round (e.g. yields learning rate decay).
+        If list lst, parameter = lst[current_round].
+        If function func, parameter = func(current_round).
+
     Returns
     -------
     callback : function
-        The requested callback function.
+        The callback that resets the parameter after the first iteration.
     """
     def callback(env):
         """internal function"""
@@ -144,22 +147,25 @@ def reset_parameter(**kwargs):
 
 def early_stopping(stopping_rounds, verbose=True):
     """Create a callback that activates early stopping.
+
+    Note
+    ----
     Activates early stopping.
-    Requires at least one validation data and one metric
-    If there's more than one, will check all of them
+    Requires at least one validation data and one metric.
+    If there's more than one, will check all of them.
 
     Parameters
     ----------
     stopping_rounds : int
-       The stopping rounds before the trend occur.
+       The possible number of rounds without the trend occurrence.
 
-    verbose : optional, bool
-        Whether to print message about early stopping information.
+    verbose : bool, optional (default=True)
+        Whether to print message with early stopping information.
 
     Returns
     -------
     callback : function
-        The requested callback function.
+        The callback that activates early stopping.
     """
     best_score = []
     best_iter = []

python-package/lightgbm/engine.py

@@ -22,8 +22,7 @@ def train(params, train_set, num_boost_round=100,
           early_stopping_rounds=None, evals_result=None,
           verbose_eval=True, learning_rates=None,
           keep_training_booster=False, callbacks=None):
-    """
-    Train with given parameters.
+    """Perform the training with given parameters.
 
     Parameters
     ----------
@@ -31,68 +30,67 @@ def train(params, train_set, num_boost_round=100,
         Parameters for training.
     train_set : Dataset
         Data to be trained.
-    num_boost_round: int
+    num_boost_round: int, optional (default=100)
         Number of boosting iterations.
-    valid_sets: list of Datasets
-        List of data to be evaluated during training
-    valid_names: list of string
-        Names of valid_sets
-    fobj : function
+    valid_sets: list of Datasets or None, optional (default=None)
+        List of data to be evaluated during training.
+    valid_names: list of string or None, optional (default=None)
+        Names of ``valid_sets``.
+    fobj : callable or None, optional (default=None)
         Customized objective function.
-    feval : function
+    feval : callable or None, optional (default=None)
         Customized evaluation function.
-        Note: should return (eval_name, eval_result, is_higher_better) of list of this
-    init_model : file name of lightgbm model or 'Booster' instance
-        model used for continued train
-    feature_name : list of str, or 'auto'
-        Feature names
-        If 'auto' and data is pandas DataFrame, use data columns name
-    categorical_feature : list of str or int, or 'auto'
-        Categorical features,
-        type int represents index,
-        type str represents feature names (need to specify feature_name as well)
-        If 'auto' and data is pandas DataFrame, use pandas categorical columns
-    early_stopping_rounds: int
-        Activates early stopping.
-        Requires at least one validation data and one metric
-        If there's more than one, will check all of them
-        Returns the model with (best_iter + early_stopping_rounds)
-        If early stopping occurs, the model will add 'best_iteration' field
-    evals_result: dict or None
-        This dictionary used to store all evaluation results of all the items in valid_sets.
-        Example: with a valid_sets containing [valid_set, train_set]
-                 and valid_names containing ['eval', 'train']
-                 and a paramater containing ('metric':'logloss')
-        Returns: {'train': {'logloss': ['0.48253', '0.35953', ...]},
-                  'eval': {'logloss': ['0.480385', '0.357756', ...]}}
-        passed with None means no using this function
-    verbose_eval : bool or int
-        Requires at least one item in evals.
-        If `verbose_eval` is True,
-            the eval metric on the valid set is printed at each boosting stage.
-        If `verbose_eval` is int,
-            the eval metric on the valid set is printed at every `verbose_eval` boosting stage.
-        The last boosting stage
-            or the boosting stage found by using `early_stopping_rounds` is also printed.
-        Example: with verbose_eval=4 and at least one item in evals,
+        Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples.
+    init_model : string or None, optional (default=None)
+        Filename of LightGBM model or Booster instance used for continue training.
+    feature_name : list of strings or 'auto', optional (default="auto")
+        Feature names.
+        If 'auto' and data is pandas DataFrame, data columns names are used.
+    categorical_feature : list of strings or int, or 'auto', optional (default="auto")
+        Categorical features.
+        If list of int, interpreted as indices.
+        If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
+        If 'auto' and data is pandas DataFrame, pandas categorical columns are used.
+    early_stopping_rounds: int or None, optional (default=None)
+        Activates early stopping. The model will train until the validation score stops improving.
+        Requires at least one validation data and one metric. If there's more than one, will check all of them.
+        If early stopping occurs, the model will add ``best_iteration`` field.
+    evals_result: dict or None, optional (default=None)
+        This dictionary used to store all evaluation results of all the items in ``valid_sets``.
+
+        Example
+        -------
+        With a ``valid_sets`` = [valid_set, train_set],
+        ``valid_names`` = ['eval', 'train']
+        and a ``params`` = ('metric':'logloss')
+        returns: {'train': {'logloss': ['0.48253', '0.35953', ...]},
+        'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
+    verbose_eval : bool or int, optional (default=True)
+        Requires at least one validation data.
+        If True, the eval metric on the valid set is printed at each boosting stage.
+        If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
+        The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
+
+        Example
+        -------
+        With ``verbose_eval`` = 4 and at least one item in evals,
         an evaluation metric is printed every 4 (instead of 1) boosting stages.
-    learning_rates: list or function
-        List of learning rate for each boosting round
-        or a customized function that calculates learning_rate
-        in terms of current number of round (e.g. yields learning rate decay)
-        - list l: learning_rate = l[current_round]
-        - function f: learning_rate = f(current_round)
-    keep_training_booster : boolean
-        Whether the return booster will be used to keep training.
-        If false, will convert into _InnerPredictor before return.
-        You can still use _InnerPredictor as init_model for future continue training.
-    callbacks : list of callback functions
+    learning_rates: list, callable or None, optional (default=None)
+        List of learning rates for each boosting round
+        or a customized function that calculates ``learning_rate``
+        in terms of current number of round (e.g. yields learning rate decay).
+    keep_training_booster : bool, optional (default=False)
+        Whether the returned Booster will be used to keep training.
+        If False, the returned value will be converted into _InnerPredictor before returning.
+        You can still use _InnerPredictor as ``init_model`` for future continue training.
+    callbacks : list of callables or None, optional (default=None)
         List of callback functions that are applied at each iteration.
         See Callbacks in Python-API.md for more information.
 
     Returns
     -------
-    booster : a trained booster model
+    booster : Booster
+        The trained Booster model.
     """
     """create predictor first"""
     for alias in ["num_boost_round", "num_iterations", "num_iteration", "num_tree", "num_trees", "num_round", "num_rounds"]:
@@ -316,68 +314,71 @@ def cv(params, train_set, num_boost_round=10,
        early_stopping_rounds=None, fpreproc=None,
        verbose_eval=None, show_stdv=True, seed=0,
        callbacks=None):
-    """
-    Cross-validation with given paramaters.
+    """Perform the cross-validation with given paramaters.
 
     Parameters
     ----------
     params : dict
-        Booster params.
+        Parameters for Booster.
     train_set : Dataset
-        Data to be trained.
-    num_boost_round : int
+        Data to be trained on.
+    num_boost_round : int, optional (default=10)
         Number of boosting iterations.
-    folds : a generator or iterator of (train_idx, test_idx) tuples
-        The train indices and test indices for each folds.
+    folds : a generator or iterator of (train_idx, test_idx) tuples or None, optional (default=None)
+        The train and test indices for the each fold.
         This argument has highest priority over other data split arguments.
-    nfold : int
+    nfold : int, optional (default=5)
         Number of folds in CV.
-    stratified : bool
-        Perform stratified sampling.
-    shuffle: bool
-        Whether shuffle before split data
-    metrics : string or list of strings
-        Evaluation metrics to be watched in CV.
-        If `metrics` is not None, the metric in `params` will be overridden.
-    fobj : function
+    stratified : bool, optional (default=True)
+        Whether to perform stratified sampling.
+    shuffle: bool, optional (default=True)
+        Whether to shuffle before splitting data.
+    metrics : string, list of strings or None, optional (default=None)
+        Evaluation metrics to be monitored while CV.
+        If not None, the metric in ``params`` will be overridden.
+    fobj : callable or None, optional (default=None)
         Custom objective function.
-    feval : function
+    feval : callable or None, optional (default=None)
         Custom evaluation function.
-    init_model : file name of lightgbm model or 'Booster' instance
-        model used for continued train
-    feature_name : list of str, or 'auto'
-        Feature names
-        If 'auto' and data is pandas DataFrame, use data columns name
-    categorical_feature : list of str or int, or 'auto'
-        Categorical features,
-        type int represents index,
-        type str represents feature names (need to specify feature_name as well)
-        If 'auto' and data is pandas DataFrame, use pandas categorical columns
-    early_stopping_rounds: int
+    init_model : string or None, optional (default=None)
+        Filename of LightGBM model or Booster instance used for continue training.
+    feature_name : list of strings or 'auto', optional (default="auto")
+        Feature names.
+        If 'auto' and data is pandas DataFrame, data columns names are used.
+    categorical_feature : list of strings or int, or 'auto', optional (default="auto")
+        Categorical features.
+        If list of int, interpreted as indices.
+        If list of strings, interpreted as feature names (need to specify ``feature_name`` as well).
+        If 'auto' and data is pandas DataFrame, pandas categorical columns are used.
+    early_stopping_rounds: int or None, optional (default=None)
         Activates early stopping. CV error needs to decrease at least
-        every <early_stopping_rounds> round(s) to continue.
+        every ``early_stopping_rounds`` round(s) to continue.
         Last entry in evaluation history is the one from best iteration.
-    fpreproc : function
-        Preprocessing function that takes (dtrain, dtest, param)
+    fpreproc : callable or None, optional (default=None)
+        Preprocessing function that takes (dtrain, dtest, params)
         and returns transformed versions of those.
-    verbose_eval : bool, int, or None, default None
+    verbose_eval : bool, int, or None, optional (default=None)
         Whether to display the progress.
         If None, progress will be displayed when np.ndarray is returned.
-        If True, progress will be displayed at boosting stage.
-        If an integer is given,
-            progress will be displayed at every given `verbose_eval` boosting stage.
-    show_stdv : bool, default True
+        If True, progress will be displayed at every boosting stage.
+        If int, progress will be displayed at every given ``verbose_eval`` boosting stage.
+    show_stdv : bool, optional (default=True)
         Whether to display the standard deviation in progress.
-        Results are not affected, and always contains std.
-    seed : int
+        Results are not affected by this parameter, and always contains std.
+    seed : int, optional (default=0)
         Seed used to generate the folds (passed to numpy.random.seed).
-    callbacks : list of callback functions
+    callbacks : list of callables or None, optional (default=None)
         List of callback functions that are applied at each iteration.
         See Callbacks in Python-API.md for more information.
 
     Returns
     -------
-    evaluation history : list(string)
+    eval_hist : dict
+        Evaluation history.
+        The dictionary has the following format:
+        {'metric1-mean': [values], 'metric1-stdv': [values],
+        'metric2-mean': [values], 'metric1-stdv': [values],
+        ...}.
     """
     if not isinstance(train_set, Dataset):
         raise TypeError("Traninig only accepts Dataset object")

python-package/lightgbm/plotting.py

@@ -24,45 +24,50 @@ def plot_importance(booster, ax=None, height=0.2,
                     xlabel='Feature importance', ylabel='Features',
                     importance_type='split', max_num_features=None,
                     ignore_zero=True, figsize=None, grid=True, **kwargs):
-    """Plot model feature importances.
+    """Plot model's feature importances.
 
     Parameters
     ----------
     booster : Booster or LGBMModel
-        Booster or LGBMModel instance
-    ax : matplotlib Axes
-        Target axes instance. If None, new figure and axes will be created.
-    height : float
-        Bar height, passed to ax.barh()
-    xlim : tuple of 2 elements
-        Tuple passed to axes.xlim()
-    ylim : tuple of 2 elements
-        Tuple passed to axes.ylim()
-    title : str
-        Axes title. Pass None to disable.
-    xlabel : str
-        X axis title label. Pass None to disable.
-    ylabel : str
-        Y axis title label. Pass None to disable.
-    importance_type : str
-        How the importance is calculated: "split" or "gain"
-        "split" is the number of times a feature is used in a model
-        "gain" is the total gain of splits which use the feature
-    max_num_features : int
+        Booster or LGBMModel instance which feature importance should be plotted.
+    ax : matplotlib.axes.Axes or None, optional (default=None)
+        Target axes instance.
+        If None, new figure and axes will be created.
+    height : float, optional (default=0.2)
+        Bar height, passed to ``ax.barh()``.
+    xlim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.xlim()``.
+    ylim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.ylim()``.
+    title : string or None, optional (default="Feature importance")
+        Axes title.
+        If None, title is disabled.
+    xlabel : string or None, optional (default="Feature importance")
+        X-axis title label.
+        If None, title is disabled.
+    ylabel : string or None, optional (default="Features")
+        Y-axis title label.
+        If None, title is disabled.
+    importance_type : string, optional (default="split")
+        How the importance is calculated.
+        If "split", result contains numbers of times the feature is used in a model.
+        If "gain", result contains total gains of splits which use the feature.
+    max_num_features : int or None, optional (default=None)
         Max number of top features displayed on plot.
-        If None or smaller than 1, all features will be displayed.
-    ignore_zero : bool
-        Ignore features with zero importance
-    figsize : tuple of 2 elements
-        Figure size
-    grid : bool
-        Whether add grid for axes
-    **kwargs :
-        Other keywords passed to ax.barh()
+        If None or <1, all features will be displayed.
+    ignore_zero : bool, optional (default=True)
+        Whether to ignore features with zero importance.
+    figsize : tuple of 2 elements or None, optional (default=None)
+        Figure size.
+    grid : bool, optional (default=True)
+        Whether to add a grid for axes.
+    **kwargs : other parameters
+        Other parameters passed to ``ax.barh()``.
 
     Returns
     -------
-    ax : matplotlib Axes
+    ax : matplotlib.axes.Axes
+        The plot with model's feature importances.
     """
     try:
         import matplotlib.pyplot as plt
@@ -133,34 +138,40 @@ def plot_metric(booster, metric=None, dataset_names=None,
     Parameters
     ----------
     booster : dict or LGBMModel
-        Evals_result recorded by lightgbm.train() or LGBMModel instance
-    metric : str or None
+        Dictionary returned from ``lightgbm.train()`` or LGBMModel instance.
+    metric : string or None, optional (default=None)
         The metric name to plot.
         Only one metric supported because different metrics have various scales.
-        Pass None to pick `first` one (according to dict hashcode).
-    dataset_names : None or list of str
-        List of the dataset names to plot.
-        Pass None to plot all datasets.
-    ax : matplotlib Axes
-        Target axes instance. If None, new figure and axes will be created.
-    xlim : tuple of 2 elements
-        Tuple passed to axes.xlim()
-    ylim : tuple of 2 elements
-        Tuple passed to axes.ylim()
-    title : str
-        Axes title. Pass None to disable.
-    xlabel : str
-        X axis title label. Pass None to disable.
-    ylabel : str
-        Y axis title label. Pass None to disable. Pass 'auto' to use `metric`.
-    figsize : tuple of 2 elements
-        Figure size
-    grid : bool
-        Whether add grid for axes
+        If None, first metric picked from dictionary (according to hashcode).
+    dataset_names : list of strings or None, optional (default=None)
+        List of the dataset names which are used to calculate metric to plot.
+        If None, all datasets are used.
+    ax : matplotlib.axes.Axes or None, optional (default=None)
+        Target axes instance.
+        If None, new figure and axes will be created.
+    xlim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.xlim()``.
+    ylim : tuple of 2 elements or None, optional (default=None)
+        Tuple passed to ``ax.ylim()``.
+    title : string or None, optional (default="Metric during training")
+        Axes title.
+        If None, title is disabled.
+    xlabel : string or None, optional (default="Iterations")
+        X-axis title label.
+        If None, title is disabled.
+    ylabel : string or None, optional (default="auto")
+        Y-axis title label.
+        If 'auto', metric name is used.
+        If None, title is disabled.
+    figsize : tuple of 2 elements or None, optional (default=None)
+        Figure size.
+    grid : bool, optional (default=True)
+        Whether to add a grid for axes.
 
     Returns
     -------
-    ax : matplotlib Axes
+    ax : matplotlib.axes.Axes
+        The plot with metric's history over the training.
     """
     try:
         import matplotlib.pyplot as plt
@@ -298,48 +309,52 @@ def create_tree_digraph(booster, tree_index=0, show_info=None,
                         name=None, comment=None, filename=None, directory=None,
                         format=None, engine=None, encoding=None, graph_attr=None,
                         node_attr=None, edge_attr=None, body=None, strict=False):
-    """Create a digraph of specified tree.
+    """Create a digraph representation of specified tree.
 
-    See:
-      - http://graphviz.readthedocs.io/en/stable/api.html#digraph
+    Note
+    ----
+    For more information please visit
+    http://graphviz.readthedocs.io/en/stable/api.html#digraph.
 
     Parameters
     ----------
-    booster : Booster, LGBMModel
+    booster : Booster or LGBMModel
         Booster or LGBMModel instance.
-    tree_index : int, default 0
-        Specify tree index of target tree.
-    show_info : list
-        Information shows on nodes.
-        options: 'split_gain', 'internal_value', 'internal_count' or 'leaf_count'.
-    name : str
+    tree_index : int, optional (default=0)
+        The index of a target tree to convert.
+    show_info : list or None, optional (default=None)
+        What information should be showed on nodes.
+        Possible values of list items: 'split_gain', 'internal_value', 'internal_count', 'leaf_count'.
+    name : string or None, optional (default=None)
         Graph name used in the source code.
-    comment : str
+    comment : string or None, optional (default=None)
         Comment added to the first line of the source.
-    filename : str
-        Filename for saving the source (defaults to name + '.gv').
-    directory : str
+    filename : string or None, optional (default=None)
+        Filename for saving the source.
+        If None, ``name`` + '.gv' is used.
+    directory : string or None, optional (default=None)
         (Sub)directory for source saving and rendering.
-    format : str
+    format : string or None, optional (default=None)
         Rendering output format ('pdf', 'png', ...).
-    engine : str
+    engine : string or None, optional (default=None)
         Layout command used ('dot', 'neato', ...).
-    encoding : str
+    encoding : string or None, optional (default=None)
         Encoding for saving the source.
-    graph_attr : dict
-        Mapping of (attribute, value) pairs for the graph.
-    node_attr : dict
+    graph_attr : dict or None, optional (default=None)
+        Mapping of (attribute, value) pairs set for the graph.
+    node_attr : dict or None, optional (default=None)
         Mapping of (attribute, value) pairs set for all nodes.
-    edge_attr : dict
+    edge_attr : dict or None, optional (default=None)
         Mapping of (attribute, value) pairs set for all edges.
-    body : list of str
-        Iterable of lines to add to the graph body.
-    strict : bool
-        Iterable of lines to add to the graph body.
+    body : list of strings or None, optional (default=None)
+        Lines to add to the graph body.
+    strict : bool, optional (default=False)
+        Whether rendering should merge multi-edges.
 
     Returns
     -------
-    graph : graphviz Digraph
+    graph : graphviz.Digraph
+        The digraph representation of specified tree.
     """
     if isinstance(booster, LGBMModel):
         booster = booster.booster_
@@ -376,27 +391,29 @@ def plot_tree(booster, ax=None, tree_index=0, figsize=None,
 
     Parameters
     ----------
-    booster : Booster, LGBMModel
-        Booster or LGBMModel instance.
-    ax : matplotlib Axes
-        Target axes instance. If None, new figure and axes will be created.
-    tree_index : int, default 0
-        Specify tree index of target tree.
-    figsize : tuple of 2 elements
+    booster : Booster or LGBMModel
+        Booster or LGBMModel instance to be plotted.
+    ax : matplotlib.axes.Axes or None, optional (default=None)
+        Target axes instance.
+        If None, new figure and axes will be created.
+    tree_index : int, optional (default=0)
+        The index of a target tree to plot.
+    figsize : tuple of 2 elements or None, optional (default=None)
         Figure size.
-    graph_attr : dict
-        Mapping of (attribute, value) pairs for the graph.
-    node_attr : dict
+    graph_attr : dict or None, optional (default=None)
+        Mapping of (attribute, value) pairs set for the graph.
+    node_attr : dict or None, optional (default=None)
         Mapping of (attribute, value) pairs set for all nodes.
-    edge_attr : dict
+    edge_attr : dict or None, optional (default=None)
         Mapping of (attribute, value) pairs set for all edges.
-    show_info : list
-        Information shows on nodes.
-        options: 'split_gain', 'internal_value', 'internal_count' or 'leaf_count'.
+    show_info : list or None, optional (default=None)
+        What information should be showed on nodes.
+        Possible values of list items: 'split_gain', 'internal_value', 'internal_count', 'leaf_count'.
 
     Returns
     -------
-    ax : matplotlib Axes
+    ax : matplotlib.axes.Axes
+        The plot with single tree.
     """
     try:
         import matplotlib.pyplot as plt

python-package/lightgbm/sklearn.py

@@ -188,7 +188,10 @@ class LGBMModel(_LGBMModelBase):
             Whether to print messages while running boosting.
         **kwargs : other parameters
             Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.
-            Note: **kwargs is not supported in sklearn, it may cause unexpected issues.
+
+            Note
+            ----
+            **kwargs is not supported in sklearn, it may cause unexpected issues.
 
         Attributes
         ----------
@@ -201,13 +204,13 @@ class LGBMModel(_LGBMModelBase):
         best_score_ : dict or None
             The best score of fitted model.
         best_iteration_ : int or None
-            The best iteration of fitted model if `early_stopping_rounds` has been specified.
+            The best iteration of fitted model if ``early_stopping_rounds`` has been specified.
         objective_ : string or callable
             The concrete objective used while fitting this model.
         booster_ : Booster
             The underlying Booster of this model.
         evals_result_ : dict or None
-            The evaluation results if `early_stopping_rounds` has been specified.
+            The evaluation results if ``early_stopping_rounds`` has been specified.
         feature_importances_ : array of shape = [n_features]
             The feature importances (the higher, the more important the feature).
 
@@ -319,7 +322,7 @@ class LGBMModel(_LGBMModelBase):
             If callable, it should be a custom evaluation metric, see note for more details.
         early_stopping_rounds : int or None, optional (default=None)
             Activates early stopping. The model will train until the validation score stops improving.
-            Validation error needs to decrease at least every `early_stopping_rounds` round(s)
+            Validation error needs to decrease at least every ``early_stopping_rounds`` round(s)
             to continue training.
         verbose : bool, optional (default=True)
             If True and an evaluation set is used, writes the evaluation progress.
@@ -560,7 +563,9 @@ class LGBMModel(_LGBMModelBase):
     def feature_importances_(self):
         """Get feature importances.
 
-        Note: feature importance in sklearn interface used to normalize to 1,
+        Note
+        ----
+        Feature importance in sklearn interface used to normalize to 1,
         it's deprecated after 2.0.4 and same as Booster.feature_importance() now.
         """
         if self._n_features is None: