Confusion Matrix
As one of the beginning steps in machine learning, it’s important to know what are the metrics that we need to observe to measure our machine learning model’s performance. Accuracy and Precision are the examples. But how can we measure the metrics? Let’s prepare our Confusion Matrix.
One of the stages in building the model is to test the model’s prediction against the actual data. Suppose that our test result is like this:
Prediction  1  0  1  0  1  1  1  1 

Actual  1  1  1  0  0  1  0  1 
We can transform the table into another form of table that we call confusion matrix.
Prediction  
0  1  
Actual  0  1  2 
1  1  4 
To easily describe the situation from above table, these are the commonly used terminologies:

True Negative
When our model predict that it is 0, and the actual data says it is also 0. 
False Positive
When our model predict that it is 1, while the actual data says it is 0. 
False Negative
When our model predict that it is 0, while the actual data says it is 1. 
True Positive
When our model predict that it is 1, and the actual data says it is also 1.
Above terminologies are actually very helpful for us to get a better understanding as to what actually the metrics that we are trying to measure. These are the metrics that I’m talking about.

Accuracy
This is actually the total portion of model’s correct prediction.Accuracy = (No. of True Positive + No. of True Negative) / Total Prediction
In our case, the model accuracy will be:
5 / 8 = 0.625

Precision
This metric will tell you how good the model is in predicting the correct true prediction.Precision = No. of True Positive / (No. of True Positive + No. of False Positive)
In our case, the model precision will be:
4 / (4 + 2) = 0. 667
. So if we have a spam detection system with precision 0.667, we can conclude that every time the system says it’s a spam, then 66.7% of the time, the prediction is correct. 
Recall (Sensitivity)
This metric will tell you how good the model is in predicting the positives data.Recall = No. of True Positive / (No. of True Positive + No. of False Negative)
In our case, the model recall will be:
4 / (4 + 1) = 0.8
. If our spam detection system has a recall 0.8, we can conclude that if we have actual datasets consiting of 100 spam comments and 200 ham comments, then potentially it could only detect 80 spam comments (80% probability) out of 100 spam comments. 
Specificity
This metric will tell you how good the model is in predicting the negatives data.Specificity = No. of True Negative / (No. of True Negative + No. of False Positive)
In our case, the modell specificity will be:
1 / (1 + 2) = 0.33
. If our spam detection system has a specifity 0.33, we can conclude that if we have actual datasets consiting of 100 spam comments and 200 ham comments, then potentially it could only detect 66 ham comments (33% probability) out of 200 ham comments.With that kind of performance, I can’t imagine how big the false alarm will be for the system owner. :)
Aside from those 4 metrics, there are actually 2 metrics more like FMeasure and BAcc. However, I won’t cover it in this post, as I’m still trying to undestand it comprehensively.
Try It Out
I think it will be very interesting if we can practice it with a bigger dataset. So, let’s practice our understanding about those 4 metrics with this data.
This is my result, what’s yours?
(Thanks to Fajri Koto and Yahya Eru Cakra for letting me use their presentation and data as the main source of this post)
I think that’s all what I want to share through this post. I hope it’s useful. :)