Cut off date in binary smoothly

Aug 6th, 2022
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How to cut off date in binary

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When your everyday work consists of lots of document editing, you know that every document format requires its own approach and sometimes specific applications. Handling a seemingly simple binary file can sometimes grind the whole process to a halt, especially if you are trying to edit with inadequate tools. To avoid this sort of difficulties, find an editor that can cover all of your needs regardless of the file format and cut off date in binary without roadblocks.

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How to Cut off date in binary

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[Music] hello and welcome back to tech online its a mesa christopher to take you through a data visualization with the binary dates ranges so with challenge is what we mean uh if you have a report what youre looking at it is a line chat showing the progress or a trend of an event or revenue like as you see our data having the date capture and the revenue but we have dates which are notified somewhere like uh uh bookmarked because there was a promo going on a promo giving lets say to customers and youll need to visualize the report or your graph and it should explain to the the audience that you see between this day and this we had this promo we had this and this going on uh like say lets say like an event or currency so this is what we are going to show in our report then on top of that you can have a comment you dont have to explain everything but you can have a comment to explain the the date ranges so guys uh stay on up to the end of this video i show you how you can do this

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The logistic regression assigns each row a probability of bring True and then makes a prediction for each row where that prbability is = 0.5 i.e. 0.5 is the default threshold.
A cut-off threshold is used, mainly for cost or burden reasons, to exclude from the target population (hence from the frame) units contributing very little to the requested statistics, small businesses for instance.
For determination of the cut-off value, we need to know the pretest probability of the disease of interest as well as the costs incurred by misdiagnosis. This means that even for a certain diagnostic test, the cut-off value is not universal and should be determined for each region and for each disease condition.
The Threshold or Cut-off represents in a binary classification the probability that the prediction is true. It represents the tradeoff between false positives and false negatives.
From the curve above, 0.3 or slightly more is the optimum point to take it as a cutoff probability. Well consider 0.3 as the cutoff for ease of understanding. Lets predict the model using this 0.3 as the final cutoff. Lets find out the accuracy, specificity, sensitivity, and confusion matrix as well.
For determination of the cut-off value, we need to know the pretest probability of the disease of interest as well as the costs incurred by misdiagnosis. This means that even for a certain diagnostic test, the cut-off value is not universal and should be determined for each region and for each disease condition.
The standard cutoff is 0.5, which means that if the predicted probability is greater than 0.5, that observation is classified as a positive (or simply as a 1).
Choose a cutoff value c that represents the relative costs. With a good model, if you set a cutoff of c = 0.998 you have the corresponding cost of a false negative as 0.002, and you are evaluating the cost of a false positive as almost 500 times that of a false negative.

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