Readme is added

README.md

@@ -1,11 +1,11 @@
 ---
 title: MCC
 datasets:
--  
+-  dataset
 tags:
 - evaluate
 - metric
-description: "TODO: add a description here"
+description: "Matthews correlation coefficient (MCC) is a correlation coefficient used in machine learning as a measure of the quality of binary and multiclass classifications."
 sdk: gradio
 sdk_version: 3.19.1
 app_file: app.py
@@ -14,37 +14,62 @@ pinned: false
 
 # Metric Card for MCC
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
 
-## Metric Description
-*Give a brief overview of this metric, including what task(s) it is usually used for, if any.*
 
+## Metric Description
+*Give a brief overview of this metric, including what task(s) it is usually used for, if any.Matthews correlation coefficient (MCC) is a correlation coefficient used in machine learning as a measure of the quality of binary and multiclass classifications. MCC takes into account true and false positives and negatives and is generally regarded as a balanced metric that can be used even if the classes are of different sizes. It can be computed with the equation:
+`MCC = (TP * TN - FP * FN) / sqrt((TP + FP)(TP + FN)(TN + FP)*(TN + FN))`
+where TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives and FN is the number of false negatives.
 ## How to Use
-*Give general statement of how to use the metric*
+*At minimum, this metric takes as input two lists, each containing ints: predictions and references.*
+
+`
+>>> mcc_metric = evaluate.load('mcc')
+>>> results = mcc_metric.compute(references=[0, 1], predictions=[0, 1])
+>>> print(results)
+["{'mcc': 1.0}"] `
+
 
-*Provide simplest possible example for using the metric*
 
 ### Inputs
-*List all input arguments in the format below*
-- **input_field** *(type): Definition of input, with explanation if necessary. State any default value(s).*
+
+- **predictions** *(list of int): The predicted labels.*
+- **references** *(list of int): The ground truth labels.*
 
 ### Output Values
 
-*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*
+**mcc(float)**: The Matthews correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. A higher MCC means a better quality of classification, 1 being a perfect prediction, 0 being a random prediction and -1 being a completely wrong prediction.
+Output Example(s):
+{'mcc': 1.0}
 
-*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*
 
-#### Values from Popular Papers
-*Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.*
 
 ### Examples
-*Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.*
+
+Example 1 - A simple example with all correct predictions
+>>> mcc_metric = evaluate.load('mcc')
+>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[1, 0, 1])
+>>> print(results)
+{'mcc': 1.0}
+
+
+Example 2 - A simple example with all incorrect predictions
+>>> mcc_metric = evaluate.load('mcc')
+>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[0, 1, 0])
+>>> print(results)
+{'mcc': -1.0}
+
+
+Example 3 - A simple example with a random prediction
+>>> mcc_metric = evaluate.load('mcc')
+>>> results = mcc_metric.compute(references=[1, 0, 1], predictions=[1, 1, 0])
+>>> print(results)
+{'mcc': 0.0}
 
 ## Limitations and Bias
 *Note any known limitations or biases that the metric has, with links and references if possible.*
 
 ## Citation
-*Cite the source where this metric was introduced.*
+- **Sklearn** - *"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"*
 
 ## Further References
-*Add any useful further references.*