Understanding Precision and Recall

Introduction

The first thought that enters our minds when creating any machine learning model is how to create a model that is accurate and an "excellent fit," as well as what problems will arise along the process. The two most crucial yet perplexing ideas in machine learning are recall and precision. Performance indicators for pattern recognition and classification in machine learning include precision and recall. Building a flawless machine learning model that produces more precise and accurate outcomes requires an understanding of these ideas. In machine learning, some models need greater recall while others need more precision. Therefore, understanding the accuracy-recall trade-off, or simply the balance between precision and recall, is crucial.

Precision and recall, two of the most challenging yet crucial machine learning topics that many professionals encounter during their whole data science and machine learning careers, will be explained in this article. But first, we must comprehend the notion of the confusion matrix.

Confusion Matrix in Machine Learning

Confusion Matrix is a tool that shows how a machine learning model performed or how it generated a prediction.

Using a confusion matrix, we can see where our model falters while trying to distinguish between two classes. A 2 x 2 matrix, where the row contains the actual truth labels and the column represents the anticipated labels, makes it easy to understand.

		Predicted
Actual		Positive	Negative
	Positive	True Positive	False Negative
	Negative	False Positive	False Negative

This matrix has four primary components that each provide a distinct measure for counting the number of accurate and inaccurate predictions. Each element comprises two words, one of which is either True or False, Positivity or negativity

The forecast is deemed right if the predicted and truth labels coincide, but the prediction is deemed erroneous when the predicted and truth labels don't match. Additionally, the predicted labels in the matrix are represented by positive and negative.

The confusion matrix has four metrics combinations, which are as follows −

• True Positive − This combo reveals how frequently a model labels a positive sample accurately as Positive.

• False Negative − This combination reveals how frequently a model identifies an improperly positive sample as a negative one.

• False Positive − The number of times a model misclassifies a negative sample as positive is indicated by the term "False Positive."

• True Negative − This combination reveals the frequency with which a model properly identifies a negative sample as such.

Precision

The proportion of accurately categorized positive samples (True Positive) to the total number of positively classified samples is known as precision (either correctly or incorrectly).

Therefore, precision aids in seeing the machine learning model's dependability in categorizing the model as positive.

Precision = TP / ( TP + FP)

We may use it to gauge how well the model can categorize positive samples. While calculating a model's precision, we must consider both the Positive and Negative data which are recognized. A model is considered to have a high recall and poor precision when it properly identifies the majority of positive samples as well as numerous false-positive samples. Both the negative and positive samples are necessary to determine how accurate a machine learning model is. All positive samples, whether rightly or mistakenly identified as positive in Precision, should be taken into account.

Recall

The recall is determined as the proportion of Positive samples that were properly identified as Positive to all Positive samples. The recall gauges how well the model can identify positive samples. The more positive samples that are identified, the larger the recall.

Recall = TP/TP+FN

Recall, in contrast to Precision, is unaffected by the number of incorrect sample classifications. Additionally, Recall will be 1 if the model labels all positive data as positive.

It aids in quantifying the number of positive samples that the ML model properly identified. Only positive samples are required for determining a model's recall; all negative samples are disregarded. A model is considered to be high accuracy, high precision, and low recall if it can only categorize a small number of positive samples while classifying a sample as positive. A machine learning model's recall is influenced by positive examples but unaffected by negative samples. Correctly identifying all positive samples is important to the recall. It does not take into account the classification of any negative samples as positive.

Why use Precision and Recall in Machine Learning Models?

All data scientists and machine learning engineers frequently ask one another this question. Depending on the kind of issue being handled, different methods of using Precision and Recall are used. Use Precision if it is necessary to categorize both positive and negative samples as positive, regardless of whether the classification was accurate or not. On the other hand, utilize Recall if your objective is to find just positive samples. Here, it shouldn't matter if negative samples are labeled correctly or wrongly.

Conclusion

For situations involving binary classification using a machine learning model, we have covered a variety of performance measures in this course, including the confusion matrix, Precision, and Recall. Additionally, we have seen several instances of how to determine a machine learning model's Precision and Recall as well as when to apply each.