minor: grammar

docs/mllib-classification-regression.md

@@ -246,11 +246,11 @@ Decision trees and their ensembles are popular methods for the machine learning
 
 ### Basic Algorithm
 
-The decision tree is a greedy algorithm performs a recursive binary partitioning of the feature space by choosing a single element from the *best split set* where each element of the set maximimizes the information gain at a tree node. In other words, the split chosen at each tree node is chosen from the set `$\underset{s}{\operatorname{argmax}} IG(D,s)$` where `$IG(D,s)$` is the information gain when a split `$s$` is applied to a dataset `$D$`.
+The decision tree is a greedy algorithm that performs a recursive binary partitioning of the feature space by choosing a single element from the *best split set* where each element of the set maximimizes the information gain at a tree node. In other words, the split chosen at each tree node is chosen from the set `$\underset{s}{\operatorname{argmax}} IG(D,s)$` where `$IG(D,s)$` is the information gain when a split `$s$` is applied to a dataset `$D$`.
 
 #### Node Impurity and Information Gain
 
-The *node impurity* is a measure of the homogeneity of the labels at the node. The current implementation provides two impurity measures for classification (Gini index and entropy) and one impurity measure for regression.
+The *node impurity* is a measure of the homogeneity of the labels at the node. The current implementation provides two impurity measures for classification (Gini index and entropy) and one impurity measure for regression (variance).
 
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@@ -277,15 +277,15 @@ The *information gain* is the difference in the parent node impurity and the wei
 
 **Continuous Features**
 
-For small datasets in single machine implementations, the split candidates for each continuous feature are typically the unique values for a feature. Some implementations sort the feature values and then use the ordered unique values as split candidates for faster tree calculations.
+For small datasets in single machine implementations, the split candidates for each continuous feature are typically the unique values for the feature. Some implementations sort the feature values and then use the ordered unique values as split candidates for faster tree calculations.
 
 Finding ordered unique feature values is computationally intensive for large distributed datasets. One can get an approximate set of split candidates by performing a quantile calculation over a sampled fraction of the data. The ordered splits create "bins" and the maximum number of such bins can be specified using the `maxBins` parameters. 
 
 Note that the number of bins cannot be greater than the number of instances `$N$` (a rare scenario since the default `maxBins` value is 100). The tree algorithm automatically reduces the number of bins if the condition is not satisfied.
 
 **Categorical Features**
 
-For `$M$` categorical features, one could come up with `$2^M-1$` split candidates. However, for binary classification, the number of split candidates can be reduced to `$M-1$` by ordering the categorical feature values by the proportion of labels falling in one of the two classes (see 9.2.4 in [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for details). For example, for a binary classification problem with one categorical feature with three categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical features are orded as A followed by C followed B (A, B, C). The two split candidates are A \| C, B and A , B \| C where \| denotes the split.
+For `$M$` categorical features, one could come up with `$2^M-1$` split candidates. However, for binary classification, the number of split candidates can be reduced to `$M-1$` by ordering the categorical feature values by the proportion of labels falling in one of the two classes (see Section 9.2.4 in [Elements of Statistical Machine Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for details). For example, for a binary classification problem with one categorical feature with three categories A, B and C with corresponding proportion of label 1 as 0.2, 0.6 and 0.4, the categorical features are orded as A followed by C followed B or A, B, C. The two split candidates are A \| C, B and A , B \| C where \| denotes the split.
 
 #### Stopping Rule