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# Confusion matrix in machine learning

Updated: Nov 7, 2022

A confusion matrix is a N x N matrix that is used to interpret model evaluation performance systematically. Here N is the number of distinct classes in the target variables that represent the Confusion matrix in machine learning.

Each row in a confusion matrix in machine learning represents an Actual Class, while each column represents a Predicted Class. The confusion matrix serves as the best platform of representation for most classification problems just like data analysis in machine learning, hence it gives you a lot of information, but sometimes you may prefer a more concise metric. For the 2 x 2 matrix we have 2 classes:-

• Class 1: Positive Class

• Class 0: Negative Class

Meaning of the parameters present in the matrix:-

Let's consider the example of a person who is a cancer patient.

• True Positive(TP): When a person is actually suffering from cancer and the predicted result is also true.

• False Positive(FP): When a person is suffering from cancer but the predicted result is false.

• False Negative(FN): When the person is not suffering but according to the predicted result he is suffering.

• True Negative(TN): When a person is not a cancer patient and the predicted result also says that.

The following metrics are derived from the confusion matrix:-

#### 1. Precision

It is used when avoiding false positives is more essential than encountering false negatives.

Precision = (Predictions actually positive)/(Total predicted positive)

= (TP)/(TP+FP)

eg: If the above value is 33%, then we can say the model is 33% precise.

#### 2. Recall

It is used when avoiding false negatives is prioritized over encountering false positives.

Precision and recall handle the imbalance dataset efficiently.

Recall= (Predictions actually positive)/(Total actual positive)

= (TP)/(TP+FN)

eg: If the above value comes out to be 66%, then we can say that only 66% of the actual positive value was predicted as positive.

If the precision is high then recall will be low and vice-versa.

But if we are not sure from the graph or metrics then we can use F1 metrics for the task, which is nothing but the harmonic mean of precision and recall.

F1 = 2/ (1/Precision + 1/Recall )

F1 is maximum when precision is equal to recall.

But if precision, recall, and F1 score yield the same result then we need to use log loss function for the task, which is nothing but the cost function for logistic regression.

The smaller the value of log loss, the better the model is.

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