Your work for a company that performs seismic research for client firms that drill for petroleum.
As a machine learning specialist, you have built a series of models that classify seismic waves to determine the seismic profile of a proposed drilling site.
You need to select the best model to use in production.
Which metric should you use to compare and evaluate your machine learning classification models against each other?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: A.
Option A is correct.
The area under the Receiver Operating Characteristic (ROC) curve is the most commonly used metric to compare classification models.
Option B is incorrect.
The Mean Square Error (MSE) is commonly used to measure regression error.
It finds the average squared error between the predicted and actual values.
It is not used to compare classification models.
Option C is incorrect.
The Mean Square Error is also commonly used to measure regression error.
It finds the average absolute distance between the predicted and target values.
It is not used to compare classification models.
Option D is incorrect.
The recall metric is the percentage of results correctly classified by a model.
This metric alone will not allow you to make a complete assessment and comparison of your models.
References:
Please see the Towards Data Science article titled Metrics For Evaluating Machine Learning Classification Models (https://towardsdatascience.com/metrics-for-evaluating-machine-learning-classification-models-python-example-59b905e079a5),
The Towards Data Science article titled How to Evaluate a Classification Machine Learning Model (https://towardsdatascience.com/how-to-evaluate-a-classification-machine-learning-model-d81901d491b1),
The Machine Learning Mastery article titled Assessing and Comparing Classifier Performance with ROC Curves (https://machinelearningmastery.com/assessing-comparing-classifier-performance-roc-curves-2/),
The Towards Data Science article titled 20 Popular Machine Learning Metrics.
Part 1: Classification & Regression Evaluation Metrics (https://towardsdatascience.com/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics-1ca3e282a2ce),
The Data School article titled Simple guide to confusion matrix terminology (https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/),
The Medium article titled Precision vs.
Recall (https://medium.com/@shrutisaxena0617/precision-vs-recall-386cf9f89488)
In this scenario, the goal is to select the best model for classifying seismic waves to determine the seismic profile of a proposed drilling site. In order to select the best model, we need to compare and evaluate the different models against each other using a suitable metric.
Out of the four options given, AUC (Area Under the ROC Curve) is the most appropriate metric for evaluating classification models. AUC measures the performance of a binary classification model, where the goal is to predict one of two possible outcomes (in this case, whether a given seismic wave is indicative of a good or bad drilling site). AUC represents the probability that a randomly selected positive example (i.e., a good drilling site) is ranked higher than a randomly selected negative example (i.e., a bad drilling site) by the model. A model with an AUC of 1.0 indicates perfect classification, while a model with an AUC of 0.5 indicates random guessing.
In contrast, Mean Square Error (MSE) and Mean Absolute Error (MAE) are metrics used for evaluating regression models, where the goal is to predict a continuous value (such as the depth of a drilling site). MSE measures the average squared difference between the predicted and actual values, while MAE measures the average absolute difference between the predicted and actual values. These metrics are not suitable for evaluating classification models.
Finally, Recall is a metric used to evaluate binary classification models, and measures the proportion of actual positive examples that are correctly identified by the model. While Recall is a useful metric for evaluating the performance of a classification model, it is not as comprehensive as AUC, which takes into account both true positive and false positive rates.
Therefore, in this scenario, the most appropriate metric for comparing and evaluating the different machine learning classification models would be Area Under the ROC Curve (AUC).