Produced in German, French, and Spanish, video2brain courses are presented by industry experts and designed for self-paced, location- and media-independent learning. Since 2002, video2brain has been Europe’s premier source for high-quality video training. Connect with your existing account (but it can be used without an account as well).Sync bookmarks and watched videos across devices. ![]() Search for courses by key words of interest. ![]() Access your selected courses, your bookmarks and your most recently visited courses using the app dashboard.Bookmark lessons that you'd like to come back to.Add courses to your personal "My video2brain" section.Download courses for use without an internet connection.Browse and access the same courses as on the website.Photoshop, InDesign, Joomla, Java, Typo3, PHP, Dreamweaver, After eEfects, Illustrator, Cinema 4d, Excel, Lightroom, Flash, Word, CSS, HTML, WordPress, AutoCAD, Sharepoint, Outlook, Photography And take full advantage of the courses you already own by saving courses to "My video2brain," downloading them for offline use, or bookmarking the most interesting lessons. Even if you're not a registered video2brain user yet, you can already view hundreds of free lessons.Ĭreating an account gives you access to even more valuable content. This value is fairly high, which indicates that the model does a good job of classifying the data into ‘Pass’ and ‘Fail’ categories.The video2brain app brings award-winning video-based training courses to your tablet or smartphone. To calculate the AUC of the curve, we can simply take the sum of all of the values in column H: A model with an AUC equal to 0.5 is no better than a model that makes random classifications. The closer AUC is to 1, the better the model. 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 more that the curve hugs the top left corner of the plot, the better the model does at classifying the data into categories.Īs we can see from the plot above, this logistic regression model does a pretty good job of classifying the data into categories. Then we’ll click the Insert tab along the top ribbon and then click Insert Scatter(X, Y) to create the following plot: To create the ROC curve, we’ll highlight every value in the range F3:G14. We’ll then copy and paste these formulas down to every cell in columns F, G, and H: Next, we’ll calculate the false positive rate (FPR), true positive rate (TPR), and the area under the curve AUC) using the following formulas: Step 3: Calculate False Positive Rate & True Positive Rate ![]() ![]() We’ll then copy and paste these formulas down to every cell in column D and column E: Next, let’s use the following formula to calculate the cumulative values for the Pass and Fail categories: The following step-by-step example shows how to create and interpret a ROC curve in Excel. This is a plot that displays the sensitivity and specificity of a logistic regression model. One way to visualize these two metrics is by creating a ROC curve, which stands for “receiver operating characteristic” curve. This is also called the “true negative rate.”
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