Update sklearn-metadata.json

source/sklearn-metadata.json

@@ -1431,7 +1431,7 @@
       },
       {
         "default": null,
-        "description": "Prior probabilities of the classes. If specified the priors are not\nadjusted according to the data.\n",
+        "description": "Prior probabilities of the classes. If specified, the priors are not\nadjusted according to the data.\n",
         "name": "class_prior",
         "option": "optional"
       }
@@ -1490,7 +1490,7 @@
       },
       {
         "default": null,
-        "description": "Prior probabilities of the classes. If specified the priors are not\nadjusted according to the data.\n",
+        "description": "Prior probabilities of the classes. If specified, the priors are not\nadjusted according to the data.\n",
         "name": "class_prior",
         "option": "optional"
       }
@@ -1807,7 +1807,7 @@
         "name": "categories"
       },
       {
-        "description": "Specifies a methodology to use to drop one of the categories per\nfeature. This is useful in situations where perfectly collinear\nfeatures cause problems, such as when feeding the resulting data\ninto a neural network or an unregularized regression.\n\nHowever, dropping one category breaks the symmetry of the original\nrepresentation and can therefore induce a bias in downstream models,\nfor instance for penalized linear classification or regression models.\n\n- None : retain all features (the default).\n- 'first' : drop the first category in each feature. If only one\ncategory is present, the feature will be dropped entirely.\n- 'if_binary' : drop the first category in each feature with two\ncategories. Features with 1 or more than 2 categories are\nleft intact.\n- array : ``drop[i]`` is the category in feature ``X[:, i]`` that\nshould be dropped.\n\n.. versionadded:: 0.21\nThe parameter `drop` was added in 0.21.\n\n.. versionchanged:: 0.23\nThe option `drop='if_binary'` was added in 0.23.\n",
+        "description": "Specifies a methodology to use to drop one of the categories per\nfeature. This is useful in situations where perfectly collinear\nfeatures cause problems, such as when feeding the resulting data\ninto an unregularized linear regression model.\n\nHowever, dropping one category breaks the symmetry of the original\nrepresentation and can therefore induce a bias in downstream models,\nfor instance for penalized linear classification or regression models.\n\n- None : retain all features (the default).\n- 'first' : drop the first category in each feature. If only one\ncategory is present, the feature will be dropped entirely.\n- 'if_binary' : drop the first category in each feature with two\ncategories. Features with 1 or more than 2 categories are\nleft intact.\n- array : ``drop[i]`` is the category in feature ``X[:, i]`` that\nshould be dropped.\n\n.. versionadded:: 0.21\nThe parameter `drop` was added in 0.21.\n\n.. versionchanged:: 0.23\nThe option `drop='if_binary'` was added in 0.23.\n",
         "name": "drop"
       },
       {