DOC small changes in outlier detection documentation
@@ -8,9 +8,9 @@ Novelty and Outlier Detection
Many applications require being able to decide whether a new observation
belongs to the same distribution as existing observations (it is an
-`inlier`), or should be considered as different (it is an outlier).
+*inlier*), or should be considered as different (it is an *outlier*).
Often, this ability is used to clean real data sets. Two important
-distinction must be made:
+distinctions must be made:
:outlier detection:
The training data contains outliers which are defined as observations that
@@ -35,7 +35,7 @@ a low density region of the training data, considered as normal in this
context.
The scikit-learn project provides a set of machine learning tools that
-can be used both for novelty or outliers detection. This strategy is
+can be used both for novelty or outlier detection. This strategy is
implemented with objects learning in an unsupervised way from the data::
estimator.fit(X_train)
@@ -77,6 +77,18 @@ not available.
The scores of abnormality of the training samples are always accessible
through the ``negative_outlier_factor_`` attribute.
+The behavior of :class:`neighbors.LocalOutlierFactor` is summarized in the
+following table.
+
+===================== ================================ =====================
+Method Outlier detection Novelty detection
+===================== ================================ =====================
+``fit_predict`` OK Not available
+``predict`` Not available Use only on new data
+``decision_function`` Not available Use only on new data
+``score_samples`` Use ``negative_outlier_factor_`` Use only on new data
+===================== ================================ =====================
+
Overview of outlier detection methods
=====================================
@@ -162,7 +174,7 @@ Outlier Detection
Outlier detection is similar to novelty detection in the sense that
the goal is to separate a core of regular observations from some
-polluting ones, called "outliers". Yet, in the case of outlier
+polluting ones, called *outliers*. Yet, in the case of outlier
detection, we don't have a clean data set representing the population
of regular observations that can be used to train any tool.
@@ -341,19 +353,7 @@ Note that ``fit_predict`` is not available in this case.
The scores of abnormality of the training samples are always accessible
through the ``negative_outlier_factor_`` attribute.
-The behavior of LOF is summarized in the following table.
-
-==================== ================================ =====================
-Method Outlier detection Novelty detection
-==================== ================================ =====================
-`fit_predict` OK Not available
-`predict` Not available Use only on test data
-`decision_function` Not available Use only on test data
-`score_samples` Use `negative_outlier_factor_` Use only on test data
-==================== ================================ =====================
-
-
-This strategy is illustrated below.
+Novelty detection with Local Outlier Factor is illustrated below.
.. figure:: ../auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png
:target: ../auto_examples/neighbors/sphx_glr_plot_lof_novelty_detection.html
