Why is ROC better than accuracy?

Accuracy vs ROC AUC
The first big difference is that you calculate accuracy on the predicted classes while you calculate ROC AUC on predicted scores. That means you will have to find the optimal threshold for your problem. Moreover, accuracy looks at fractions of correctly assigned positive and negative classes.
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Is ROC the same as accuracy?

ROC curve is a graphic presentation of the relationship between both sensitivity and specificity and it helps to decide the optimal model through determining the best threshold for the diagnostic test. Accuracy measures how correct a diagnostic test identifies and excludes a given condition.
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What is the advantage of using ROC score?

Advantages of the ROC curves:

A simple graphical representation of the diagnostic accuracy of a test: the closer the apex of the curve toward the upper left corner, the greater the discriminatory ability of the test.
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Why AUC score is better than accuracy?

Accuracy is a very commonly used metric, even in the everyday life. In opposite to that, the AUC is used only when it's about classification problems with probabilities in order to analyze the prediction more deeply. Because of that, accuracy is understandable and intuitive even to a non-technical person.
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Why is ROC AUC a good metric?

That is where ROC AUC is very popular, because the curve balances the class sizes. It's easy to achieve 99% accuracy on a data set where 99% of objects is in the same class.
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ROC and AUC, Clearly Explained!



Can AUC be greater than accuracy?

Although it has a slightly lower accuracy, note that its sensitivity is much higher at this cut-off... Finally, you cannot compare the accuracy (a performance at one threshold) with the AUC (an average performance on all possible thresholds).
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Why is AUC a good measure?

The AUC is an estimate of the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative instance. For this reason, the AUC is widely thought to be a better measure than a classification error rate based upon a single prior probability or KS statistic threshold.
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Is AUC good for Imbalanced data?

ROC AUC and Precision-Recall AUC provide scores that summarize the curves and can be used to compare classifiers. ROC Curves and ROC AUC can be optimistic on severely imbalanced classification problems with few samples of the minority class.
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What is a good ROC AUC score?

The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6.
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When should I use ROC curve?

ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
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What is ROC analysis used for?

ROC analysis is a valuable tool to evaluate diagnostic tests and predictive models. It may be used to assess accuracy quantitatively or to compare accuracy between tests or predictive models. In clinical practice, continuous measures are frequently converted to dichotomous tests.
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What is ROC used for?

The receiver operating characteristic (ROC) curve is frequently used for evaluating the performance of binary classification algorithms. It provides a graphical representation of a classifier's performance, rather than a single value like most other metrics.
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Why is ROC not accurate?

Accuracy vs ROC AUC

The first big difference is that you calculate accuracy on the predicted classes while you calculate ROC AUC on predicted scores. That means you will have to find the optimal threshold for your problem. Moreover, accuracy looks at fractions of correctly assigned positive and negative classes.
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What is accuracy in an ROC curve?

The ROC curve is a mathematical curve and not an individual number statistic. In particular, this means that the comparison of two algorithms on a dataset does not always produce an apparent order. Accuracy (= 1 – error rate) is a standard method employed to estimate training algorithms.
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Is Higher F1 score better?

In the most simple terms, higher F1 scores are generally better. Recall that F1 scores can range from 0 to 1, with 1 representing a model that perfectly classifies each observation into the correct class and 0 representing a model that is unable to classify any observation into the correct class.
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What is the difference between ROC and AUC?

ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1.
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How do you interpret ROC AUC scores?

AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.
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What ROC curve means?

A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
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Why accuracy is not good for imbalanced dataset?

Even when model fails to predict any Crashes its accuracy is still 90%. As data contain 90% Landed Safely. So, accuracy does not holds good for imbalanced data. In business scenarios, most data won't be balanced and so accuracy becomes poor measure of evaluation for our classification model.
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Why is precision-recall curve better for Imbalanced data?

Because precision and recall don't consider true negatives, the PR curve is not affected by the data imbalance.
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Is ROC curve for Imbalanced data?

ROC curves are appropriate when the observations are balanced between each class, whereas precision-recall curves are appropriate for imbalanced datasets. In both cases the area under the curve (AUC) can be used as a summary of the model performance.
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Which of the following ROC curve represents the best model?

The red curve represents the ideal curve.
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Is micro F1 equal to accuracy?

Taking a look to the formula, we may see that Micro-Average F1-Score is just equal to Accuracy. Hence, pros and cons are shared between the two measures. Both of them give more importance to big classes, because they just consider all the units together.
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How do you interpret a ROC curve in logistic regression?

The more that the ROC curve hugs the top left corner of the plot, the better the model does at classifying the data into categories. To quantify this, we can calculate the AUC (area under the curve) which tells us how much of the plot is located under the curve. The closer AUC is to 1, the better the model.
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Is AUC of 0.7 good?

AUC can be computed using the trapezoidal rule. In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.
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