[docs] fixed minor typos

docs/Parameters.rst

@@ -633,7 +633,7 @@ IO Parameters
 
    -  **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
 
-   -  **Note**: unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value.
+   -  **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
 
 -  ``num_iteration_predict`` :raw-html:`<a id="num_iteration_predict" title="Permalink to this parameter" href="#num_iteration_predict">&#x1F517;&#xFE0E;</a>`, default = ``-1``, type = int
 

include/LightGBM/config.h

@@ -588,7 +588,7 @@ struct Config {
   // desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributes to each prediction
   // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data
   // desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package <https://github.com/slundberg/shap>`__
-  // desc = **Note**: unlike the shap package, with ``pred_contrib`` we return a matrix with an extra column, where the last column is the expected value.
+  // desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
   bool predict_contrib = false;
 
   // desc = used only in ``prediction`` task

python-package/lightgbm/sklearn.py

@@ -596,7 +596,7 @@ def predict(self, X, raw_score=False, num_iteration=None,
         predicted_result : array-like of shape = [n_samples] or shape = [n_samples, n_classes]
             The predicted values.
         X_leaves : array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]
-            If ``pred_leaf=True``, the predicted leaf every tree for each sample.
+            If ``pred_leaf=True``, the predicted leaf of every tree for each sample.
         X_SHAP_values : array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes]
             If ``pred_contrib=True``, the feature contributions for each sample.
         """