j143 commented on a change in pull request #952:
URL: https://github.com/apache/systemml/pull/952#discussion_r436232635
##########
File path: dev/docs/builtins-reference.md
##########
@@ -318,7 +320,47 @@ slicefinder(X,W, y, k, paq, S);
### Usage
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
+y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
+## `confusionMatrix`-Function
+
+A `confusionMatrix` is a technique for summarizing the performance of a
classification algorithm.
+Calculating a confusion matrix can give you a better idea of what your
classification model is getting right and what types of errors it is making.
+This confusionMatrix function accepts two matrices with one column each, these
two matrices are vector for prediction and one-hot-encoded matrix respectively.
+Then it computes the max value of each vector and compare them, after whichit
calculates and returns the sum of classifications and the average of each true
class.
+
+### Usage
+```r
+confusionMatrix(P,Y)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :------ | :------------- | :--- | :---------- |
+| P | Matrix[Double] | --- |vector of prediction |
Review comment:
Can this be made consistent? - i.e., after `|` one space only, and one
space in the end. Tables syntax for other functions is good for reference.
##########
File path: dev/docs/builtins-reference.md
##########
@@ -318,7 +320,47 @@ slicefinder(X,W, y, k, paq, S);
### Usage
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
+y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
+## `confusionMatrix`-Function
+
+A `confusionMatrix` is a technique for summarizing the performance of a
classification algorithm.
+Calculating a confusion matrix can give you a better idea of what your
classification model is getting right and what types of errors it is making.
Review comment:
`you`, `your` if possible can be avoided, instead. So, it is (without
these two words!)
`a confusion matrix can give a better idea of what the classification model
is getting right.`
##########
File path: dev/docs/builtins-reference.md
##########
@@ -318,7 +320,47 @@ slicefinder(X,W, y, k, paq, S);
### Usage
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
+y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
+## `confusionMatrix`-Function
+
+A `confusionMatrix` is a technique for summarizing the performance of a
classification algorithm.
+Calculating a confusion matrix can give you a better idea of what your
classification model is getting right and what types of errors it is making.
+This confusionMatrix function accepts two matrices with one column each, these
two matrices are vector for prediction and one-hot-encoded matrix respectively.
+Then it computes the max value of each vector and compare them, after whichit
calculates and returns the sum of classifications and the average of each true
class.
+
+### Usage
+```r
+confusionMatrix(P,Y)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :------ | :------------- | :--- | :---------- |
+| P | Matrix[Double] | --- |vector of prediction |
+| Y | Matrix[Double] | --- | vector of Golden standard One
Hot Encoded|
+
+### Returns
+
+|Name | Type | Description |
+|:-----------------| :------------- | :---------- |
+|ConfusionSum | Matrix[Double] | The Confusion Matrix Sums of
classifications |
+|ConfusionAvg | Matrix[Double] | The Confusion Matrix averages of each
true class|
+
+### Example
+ #here numClasses is assigned to 1 as numClasses is directly proportional to
the
+ number of columns in the one hot data matrix, as confusion matrix accepts
only matrices with one column.
+
+```r
+numClasses = 1
Review comment:
Can this be added in the example snippet itself.
```r
# here numClasses is assigned to 1 as numClasses is directly proportional
to the
# number of columns in the one hot data matrix, as confusion matrix accepts
only matrices with one column.
```
##########
File path: dev/docs/builtins-reference.md
##########
@@ -318,7 +320,47 @@ slicefinder(X,W, y, k, paq, S);
### Usage
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
+y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
+## `confusionMatrix`-Function
+
+A `confusionMatrix` is a technique for summarizing the performance of a
classification algorithm.
+Calculating a confusion matrix can give you a better idea of what your
classification model is getting right and what types of errors it is making.
+This confusionMatrix function accepts two matrices with one column each, these
two matrices are vector for prediction and one-hot-encoded matrix respectively.
+Then it computes the max value of each vector and compare them, after whichit
calculates and returns the sum of classifications and the average of each true
class.
+
+### Usage
+```r
+confusionMatrix(P,Y)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :------ | :------------- | :--- | :---------- |
+| P | Matrix[Double] | --- |vector of prediction |
+| Y | Matrix[Double] | --- | vector of Golden standard One
Hot Encoded|
+
+### Returns
+
+|Name | Type | Description |
+|:-----------------| :------------- | :---------- |
+|ConfusionSum | Matrix[Double] | The Confusion Matrix Sums of
classifications |
+|ConfusionAvg | Matrix[Double] | The Confusion Matrix averages of each
true class|
+
+### Example
+ #here numClasses is assigned to 1 as numClasses is directly proportional to
the
+ number of columns in the one hot data matrix, as confusion matrix accepts
only matrices with one column.
Review comment:
These lines can be removed.
##########
File path: scripts/builtin/outlier.dml
##########
@@ -18,6 +18,13 @@
# under the License.
#
#-------------------------------------------------------------
+#An outlier in a probability distribution function is a number that is more
+#than 1.5 times the length of the data set away from either the lower or upper
quartiles.
+#Specifically, if a number is less than Q1−1.5×IQR or greater than Q3+1.5×IQR,
then it is an outlier.
+#
+
+
+
Review comment:
Shall we delete thi
##########
File path: dev/docs/builtins-reference.md
##########
@@ -318,7 +320,47 @@ slicefinder(X,W, y, k, paq, S);
### Usage
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
+y = X %*% rand(rows = ncol(X), cols = 1)
w = lm(X = X, y = y)
ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
```
+
+## `confusionMatrix`-Function
+
+A `confusionMatrix` is a technique for summarizing the performance of a
classification algorithm.
+Calculating a confusion matrix can give you a better idea of what your
classification model is getting right and what types of errors it is making.
+This confusionMatrix function accepts two matrices with one column each, these
two matrices are vector for prediction and one-hot-encoded matrix respectively.
+Then it computes the max value of each vector and compare them, after whichit
calculates and returns the sum of classifications and the average of each true
class.
Review comment:
These lines are long, can they be curtailed a bit, to be readable.
For example `331` line we can stop the line at `what types` and `of errors
it is making.` comes in the `332` line.
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