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Topic: What is ROC curve and how to use it for predictive modeling in R projects? Section: Expert Writers for R Projects Explain how to create ROC curve using ROCR library in R? Also, you should mention ROCR library as well. Topic: What is Logistic Regression and how to perform it in R projects? Section: Top-notch Experts for R Projects Examine the steps to perform Logistic Regression in R? Explain how to use glm library for
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“Scientific researchers from all around the world use ROC curve to analyze a sample’s response to a variable of interest and compare it with a control group. We’ve been offering high-quality ROC curve assignments in R to students all over the world. If you need ROC curve assignment help, we’re here for you.” The writing in the text doesn’t really capture the selling point of the service — a high-quality ROC curve assignment in R. It is rather an average description of what a service does and doesn’t
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ROC curve is a plotting technique in statistics that displays the area under the curve (AUC) as a function of the class labels and the threshold. ROC curve is an extension of the receiver operating characteristic (ROC) curve, which is a common metric in machine learning. When creating a ROC curve, you want to make sure you are not making the wrong predictions for the majority class. In this case, we will have a binary classification problem (class 1 vs. Class 0). Here are the steps we will follow: 1. Collect the training data
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If you want to provide ROC (receiver operating characteristic) curve assignments, then you need a special tool called ROCR package. This tool is really helpful because it provides a feature to calculate the confusion matrix, area under ROC curve (AUC), precision, recall, specificity, and other metrics. Now I would tell about the advantages of using the ROCR package, as mentioned on its documentation. Section: Advantages of Using ROCR Package in R Projects 1. Fast Calculation of AUC: With ROCR package, you