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Introduction
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Machine Learning has many methods to check for the accuracy of classification models. These models may seem to be working fine and appropriate for the situation that they were made for. However, they often may not be ideal, and just the usual metric of accuracy may not be enough to determine whether that model can be utilized for the said purpose. This makes it quite essential to have a look at other performance metrics such as ROC Curves, AUC, Precision, and Recall.
Article Overview
- What are Performance Metrics in ML Classification?
- What Types of Performance Metrics are there in ML?
- Why are Performance Metrics Important?
- Performance Metrics for Classification in ML Interview Questions/Answers
- Wrap Up
What are Performance Metrics in ML Classification?
Performance metrics are those that are able to measure the accuracy or appropriateness of a model to the degree that satisfies all the stakeholders involved in utilizing the model in a particular situation. They evaluate the ML model in the depth required by the scenario.
What Types of Performance Metrics are there in ML?
There are many types. Some of them are:
- Confusion Matrix
- ROC Curve
- Accuracy
- Recall/Sensitivity
- Precision
- F1 Score
- Specificity
Why are Performance Metrics Important?
Performance Metrics are vital since they could save many issues from arising in certain situations. For example, a model may have high accuracy, but its true positive rate may be low, and false negative rate may be high for a cancer classification model. This is problematic since patients that have cancer may not be appropriately detected even if their ratio is low in comparison to how many were actually identified correctly. In such a situation, the model must be tweaked so that its recall increases and its precision decreases.
Performance Metrics for Classification in ML Interview Questions/Answers
All the performance metrics serve different purposes and apply in different situations. As we saw in the earlier example, recall is more important for a cancer classification model than precision. Therefore, let’s dive into a few questions for Performance Metrics in Classification in ML models. Try to answer them in your head before clicking the arrow to reveal the answer.
Precision can also be called the ‘positive predictive value.’ It compares the number of correct positives predicted by your model to the total number of positives it predicts.
Precision = True Positives / (True Positives + False Positives)
True Positives + False Positives = Total Predicted Positives
It can be said that precision is also a measure of quality, exactness, or accuracy. A high precision value would mean that more or all of the positive results our model predicted were correct.
The ROC curve in ML is a graphical representation of the difference between true positive rates and the false positive rate at various limits (various thresholds). The ROC curve is also often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs. the fall-out or the probability it will trigger a false alarm (false positives).
It is the most common and intuitive performance measure and is simply the ratio of correctly predicted observations to the total observations. Usually, if we have high accuracy, then our model is at its optimum level of operation. However, although we could say that accuracy is a great measure, this is only when we have symmetric datasets where false positives and false negatives are almost the same.
Accuracy = True Positive + True Negative / (True Positive +False Positive + False Negative + True Negative)
Precision and recall are used together since they complement each other in how they describe the effectiveness of a model. The F1 score combines these two as the weighted harmonic mean of precision and recall.
F1 Score = 2 * (Precision * Recall) / (Precision + Recall)
Recall is also called sensitivity or true positive rate. It compares the correct positives that our model predicted to the actual number of positives in our data.
Recall = True Positives / (True Positives + False Positives)
True Positives + False Positive = True Actual Positives
Recall can also be termed as the measure of completeness. A high recall value would mean that our model classified most or all of the possible positive elements as positive.
A confusion matrix is also called the error matrix. It is a table that is often used to demonstrate how well our classification model is performing, i.e., classifier on a set of test data for which the true values are well-known.
It enables us to visualize the performance of an algorithm/model and also allows us to identify the confusion between different classes easily.
AUC (area under the curve). The higher the area under the curve, the better the prediction power of the model.
The idea behind the precision-recall trade-off comes into play when, for example, a person changes the limit (threshold) for determining if a class is positive or negative, it will tilt the scales. What is meant by this is that it will cause precision to increase and recall to decrease, or vice versa.
Wrap Up
To summarize all the above, the importance of performance metrics is nondebatable. They can literally end up saving lives! Their significance also lies in making appropriate business decisions or predicting where an object may be in the future with a level of assurity that may not have existed otherwise. They play a vital role in places where an error cannot be afforded. Therefore, performance metrics are deserving of their place in ML classification models.
