5 When we get the data, after data cleaning, pre-processing and wrangling, the first step we do is to feed it to an outs

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5 When We Get The Data After Data Cleaning Pre Processing And Wrangling The First Step We Do Is To Feed It To An Outs 1 (42.11 KiB) Viewed 39 times

5 When we get the data, after data cleaning, pre-processing and wrangling, the first step we do is to feed it to an outstanding model and of course, get output in probabilities. But hold on! How in the hell can we measure the effectiveness of our model? Better the effectiveness, better the performance and that's exactly what we want. And it is where the Confusion matrix comes into the limelight. Confusion Matrix is a performance measurement for machine learning classification. Suppose we have divided your result into two classes. How many terms can be defined for Class1 (Positive) and Class2 (Negative)? Define with an example. Briefly explain Accuracy rate, Recall, Precision and F-Measure with an example. Consider the following prediction table with total number of data N-95. Now find the given terms: Actual Positive, Negative and Predicted Positive and Negative, TP, TN, FP, FN, TP rate and FP rate, Accuracy, Error, Recall, Precision and F-Measure. (5 Data Classified Water Water 21 Forest 5 Urban 7 Total 33 Predicted Data Forest Urban Total 27 37 31 95 6 31 2 39 0 1 22 23