User: You are given access to make searches on the Internet. The format for making a search is: 1. The user sends a message. 2. You will decide whether to make a search. If you choose to make a search, you will output a single message, containing the word 
 (EXACTLY AS IT IS GIVEN TO YOU), followed by ONLY your web search query. After outputting your search query, you MUST output the token 
 (EXACTLY AS IT IS GIVEN TO YOU) to denote the end of the search query. Keep your search query concise as there is a 400 characters limit.
The system will then append the web search results at the end of the user message, starting with the token 
. Take note that the user CANNOT ACCESS the content under 
 - YOU should incorporate these results into your response. Additionally, If the token 
 is present in the user's message, then you MUST NOT request another web search.

IMPORTANT! You should choose to search the web ONLY if ANY of the following circumstances are met:
1. User is asking about current events or something that requires real-time information (news, sports scores, 'latest' information, etc.)
2. User is asking about some term you are unfamiliar with (e.g. if it's a little-known term, if it's a person you don't know well, etc.)
3. User is asking about anything that involves numerical facts (e.g. distances between places, population count, sizes, date and time, etc.)
4. User explicitly asks you to search or provide links to references.
If the user's query does NOT require a web search, YOU MUST NEVER run one for no reason.
However, NEVER refuse to search the web if the user asks you to provide such information.

When using web search results, you are encouraged to cite references where appropriate, using inline links in standard markdown format.
You are also allowed to make use of web search results from previous messages, if they are STRONGLY RELEVANT to the current message.

When you are not running a web search, respond completely as per normal - there is NO NEED to mention that you're not searching. 
Do your best to be informative. However, you MUST NOT make up any information. If you think you might not know something, search for it.

Assistant: Understood. I will strictly follow these instructions in this conversation.
User: AUROC-curve meaning??


 for "AUROC-curve meaning??":

1.
URL: https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5
Title: Understanding AUC - ROC Curve - Towards Data Science
Content: It is also written as <b>AUROC</b> (Area Under the Receiver Operating Characteristics) Note: For better understanding, I suggest you read my article about Confusion Matrix. This blog aims to answer the following questions: 1. What is the AUC - ROC <b>Curve</b>? 2. Defining terms used in AUC and ROC <b>Curve</b>. 3. How to speculate the performance of the model? 4.

2.
URL: https://glassboxmedicine.com/2019/02/23/measuring-performance-auc-auroc/
Title: Measuring Performance: AUC (AUROC) - Glass Box
Content: Learn how to calculate and interpret the area under the receiver operating characteristic (<b>AUROC</b>), a metric for evaluating classification models. See examples, code, and limitations of <b>AUROC</b> for imbalanced data.

3.
URL: https://www.geeksforgeeks.org/auc-roc-curve/
Title: AUC ROC Curve in Machine Learning - GeeksforGeeks
Content: Learn how to evaluate the performance of a binary classification model using the AUC-ROC <b>curve</b>, a graphical representation of the area under the receiver operating characteristic <b>curve</b>. Understand the key terms, the relationship between sensitivity, specificity, FPR, and threshold, and the implementation of AUC-ROC using two models.

4.
URL: https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/
Title: The Complete Guide to AUC and Average Precision ... - Glass Box
Content: Learn how to use AUC (<b>AUROC</b>) and average precision to evaluate classifiers on balanced data, with simulations and plots. AUC is the probability that a positive example has a higher predicted probability than a negative one, and average precision is the area under the precision-recall <b>curve</b>.

5.
URL: https://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it
Title: What does AUC stand for and what is it? - Cross Validated
Content: In this figure, the blue area corresponds to the Area Under the <b>curve</b> of the Receiver Operating Characteristic (<b>AUROC</b>). The dashed line in the diagonal we present the ROC <b>curve</b> of a random predictor: it has an <b>AUROC</b> of 0.5. The random predictor is commonly used as a baseline to see whether the model is useful.

6.
URL: https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/
Title: Guide to AUC ROC Curve in Machine Learning - Analytics Vidhya
Content: Learn what AUC ROC <b>curve</b> is, how it measures the performance of binary classification models, and how to interpret its values. See examples, definitions, and visualizations of AUC ROC <b>curve</b> and related metrics like sensitivity, specificity, and confusion matrix.

7.
URL: https://medium.com/@mangammanoj/auc-what-is-its-meaning-c6ccbfb99892
Title: AUC Decoded: Its Meaning &amp; Intuition. Clearly Explained!
Content: Image by author. What is <b>AUROC</b>? <b>AUROC</b> is simply the Area Under the ROC <b>curve</b>. Since the AUC is a portion of the area of the unit square, its value will always be between 0 and 1, with a higher ...

8.
URL: https://towardsdatascience.com/interpreting-roc-curve-and-roc-auc-for-classification-evaluation-28ec3983f077
Title: Interpreting ROC Curve and ROC AUC for Classification Evaluation
Content: The <b>AUROC</b> <b>Curve</b> (Area Under ROC <b>Curve</b>) or simply ROC AUC Score, is a metric that allows us to compare different ROC <b>Curves</b>. The green line is the lower limit, and the area under that line is 0.5, and the perfect ROC <b>Curve</b> would have an area of 1. As closer as our model's ROC AUC is from 1, the better it is in separating classes and making ...

9.
URL: https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
Title: Classification: ROC and AUC | Machine Learning - Google Developers
Content: Learn how to use receiver-operating characteristic <b>curve</b> (ROC) and area under the <b>curve</b> (AUC) to evaluate a binary classification model across all possible thresholds. See examples, diagrams, and exercises to understand the concepts and tradeoffs of ROC and AUC.

10.
URL: https://vitalflux.com/roc-curve-auc-python-false-positive-true-positive-rate/
Title: ROC Curve &amp; AUC Explained with Python Examples - Data Analytics
Content: <b>AUROC</b>: <b>AUROC</b> represents Area Under ROC <b>Curve</b> (<b>AUROC</b>). The area under ROC <b>curve</b> is computed to characterise the performance of a classification model. The area under the <b>curve</b> (AUC, or ROC AUC) is used as an overall measure of accuracy. ... Lower value of FPR would mean that the value of false positive is very low which means almost all ...

11.
URL: https://mlwhiz.com/blog/2021/02/03/roc-auc-curves-explained/
Title: A Layman&#x27;s guide to ROC Curves And AUC - MLWhiz
Content: Learn what ROC <b>curves</b> and AUC are, how they are calculated, and why they are useful for evaluating binary classifiers. Understand the difference between accuracy, threshold invariance, and scale invariance, and see examples and code in Python.

12.
URL: https://en.wikipedia.org/wiki/Receiver_operating_characteristic
Title: Receiver operating characteristic - Wikipedia
Content: A receiver operating characteristic <b>curve</b>, or ROC <b>curve</b>, is a graphical plot that illustrates the performance of a binary classifier model at varying threshold values. The ROC <b>curve</b> is the plot of the true positive rate against the false positive rate at each threshold setting, and it can be used for various applications in machine learning, data mining, and decision making.

13.
URL: https://www.evidentlyai.com/classification-metrics/explain-roc-curve
Title: How to explain the ROC curve and ROC AUC score? - Evidently AI
Content: Learn how to plot the ROC <b>curve</b> and compute the ROC AUC score, two popular metrics to evaluate binary classifiers. The web page covers the definitions, formulas, examples, and interpretations of TPR, FPR, and AUC.

14.
URL: https://mlu-explain.github.io/roc-auc/
Title: ROC &amp; AUC - MLU-Explain
Content: Learn how to use ROC <b>curves</b> and AUC to evaluate the performance of a classifier by plotting its true positive rate and false positive rate across all possible thresholds. See examples, comparisons, and how to select the optimal threshold for your model.

15.
URL: https://riptutorial.com/machine-learning/example/14446/area-under-the-curve-of-the-receiver-operating-characteristic--auroc-
Title: machine-learning Tutorial =&gt; Area Under the Curve of the Receiver...
Content: <b>AUROC</b> is a metric to evaluate a classifier's performance based on the area under the receiver operating characteristic <b>curve</b>. Learn how to compute it from the confusion matrix, true positive rate and false positive rate, and see examples and graphs.


Assistant: