A) Compute the ROC curve for this classifier and report the coordinates of the curve. To this end, usethresholding to turn the predicted values into the 0/1 classes and for each threshold, compute confusionmatrices and the usual performance measures (FPR, TPR). The list of all these performance measuresgives the coordinates of the curve.B) Compute the area under the curve and write it here AUCof this classifier.Briefly comment on the performance Q2 ClassificationA logistic classifier was fitted to data, and six fresh observations were used to assess the performance of thisclassifier. Here are the fitted logistic probabilities and the observations (Y values).##Logistic predictions Fresh data values## [1,]0.900## [2,]0.270## [3,]0.370## [4,]0.571## [5,]0.931## [6,]0.200

1. **Threshold Selection**: We start with the given logistic probabilities and need to decide on…

Answered: A) Compute the ROC curve for this…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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This assignment requires you to conduct research and compile a list of typical anti-patterns that arise, as well as categorize which anti-patterns come under the Design, Development, and Architecture stages. Make certain that you provide at least three examples of how anti patterns can arise under each phase of the process.
Please explain plain as possible [For this question, you can use a program such as Excel or Word (paste into the quiz, then format at needed), or use the table formatting tools in the Canvas Toolbar in the quiz question to create your matrix.] Using the scenario information, illustrate a factorial matrix with identifiers for each side of the matrix, and the variable identifications in each box of the table. The goal is that your reader should be able to visually see what you are researching based on the matrix table. Do not copy and paste the matrix from SPSS or other automatically generated format. You are to create the table manually. However, it does not need to be complicated. Simply identify a letter or code that illustrates your variables in the scenario, and be sure your reader knows what they mean. An example is provided below. The goal here is not that you produce a beautifully formatted table for this quiz, but that your information is clearly understandable to your…
You want to develop a program for the FBI to assist in crime investigation. In such crimes, investigators create a crime board to model the relationship between the different suspects, pieces of evidence and witnesses involved in the crime. You need to model these profiles in a way to answer questions such as -Does suspect A have a direct or indirect relation with suspect B? Describe a 1. data structure that you should use. 2. How you will set it up (initial state ). 3. Briefly state how your algorithm should function.
Consider the case of a shop that sells CDs in cash. The CDs are of three types: Movie CDs, Software CDs and Music CDs. For each of the following CD types, the required attributes are:Movie CD: Movie Title, Year of Release, Lead Actor Name, CD price and CD quantitySoftware CD: Software Name, Edition, Year of Release, CD price and CD quantityMusic CD: Album Title, Year of Release, Number of Songs, Format, CD price and CD quantity. Write a java statement to declare a list (you may follow any) of type CD and instantiate usingHashSet to avoid duplicate entries. Does detecting the same entry requires any method overriding? Apply sorting on the year of release.
5 of 5 You are evaluating a classification model and notice that the error on the test set is significantly greater than on the training set. You determine that the model is overfitting the training data. What could you do to prevent overfitting? Reduce the number of features to model Increase the train/test split ratio Decrease the train/test split ratio Use a different error function NEXT
Consider the case of a shop that sells CDs in cash. The CDs are of three types: Movie CDs, Software CDs and Music CDs. For each of the following CD types, the required attributes are:Movie CD: Movie Title, Year of Release, Lead Actor Name, CD price and CD quantitySoftware CD: Software Name, Edition, Year of Release, CD price and CD quantityMusic CD: Album Title, Year of Release, Number of Songs, Format, CD price and CD quantity All objects of the classes cannot change their state once they are instantiated. How will you implement this?
what will be the answer of final feature map?
Give the configuration after applying the appropriate transition function, using the symbols a, b, c. Only apply the transition function once. PICK THE CORRECT TRANSITION FUNCTION, and apply it, giving your final configuration Assume your original configuration is: abbaqibba Transition functions available (choose and apply only ONE): • 8(q₁, a) = (q2, a, R) • 8(q₁, b) = (93, C, L) • 8(93, a) = (q4, C, R)
Consider the case of a shop that sells CDs in cash. The CDs are of three types: Movie CDs, Software CDs and Music CDs. For each of the following CD types, the required attributes are:Movie CD: Movie Title, Year of Release, Lead Actor Name, CD price and CD quantitySoftware CD: Software Name, Edition, Year of Release, CD price and CD quantityMusic CD: Album Title, Year of Release, Number of Songs, Format, CD price and CD quantity. The variable of CDType can hold a fixed set of values that are Movie, Software and Music, what data type you recommend is suitable to store this information to ensure typesafety firmly. Declare and initialize the CDType variable.
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Step 1. Intersection over Union def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample def evaluate_sample(target_pred, target_true, iou_threshold=0.5): ----> return results Step 3. Evaluate Model def evaluate(model, test_loader, device): ---> return auc(recall, precision) Step 4. Train function Now define the functions for training the model. def train_one_epoch(model, train_dataloader, optimizer, device): # YOUR CODE HERE # TRAIN YOUR MODEL ON THE train_dataloader pass def train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs=10): for epoch inrange(n_epochs): model.eval() test_auc = evaluate(model, val_dataloader, device=device) print("AUC ON TEST: {:.4f}".format(test_auc)) model.train() train_one_epoch(model, train_dataloader, optimizer, device=device) #for refrence and data detail go to --->…
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