19. A data scientist has trained a binary classification model to detect whether an email is spam or not. He now wants to evaluate the perfomance of the model on a test dataset. The test dataset contains 100 samples. 80 of the samples in the test dataset are records of emails which are not spam. The model correctly predicted 70 emails as not spam. It also correctly predicted 12 emails as spam. Which of the following statements about the metrics of the model is true? - Recall for spam class is 0.6 and recall for not spam class is 0.875- Accuracy for the model is 82 percent- Precision for spam class in 0.6 and recall for not spam class in 0.875- Precision for the spam class is 0.6 and precision for the not spam class is 0.875- Recall for the spam class is 0.545 and recall for the not spam class is 0.8972 of the 5 listed3 of the 5 listed4 of the 5 listedNone of the listed1 of the 5 liste

You are tasked with evaluating a simple binary classification model using a confusion matrix. The dataset involves predicting whether a given email is spam or not. To better understand the model's performance, you plan to extract specific metrics from the confusion matrix, specifically True Positives (TP) and False Positives (FP). Below is your initial code setup:from sklearn.metrics import confusion_matrixfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.datasets import make_classification# Generate synthetic binary classification dataX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the dataX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Train a Random Forest classifierclassifier = RandomForestClassifier(random_state=42)classifier.fit(X_train, y_train)# Predict the test set resultsy_pred = classifier.predict(X_test)# Generate the confusion matrixcm = confusion_matrix(y_test, y_pred)# [Your code here] - Extract and print True Positives and False PositivesWhich snippet of code correctly extracts and prints the True Positives (TP) and False Positives (FP) from the confusion matrix?Which snippet of code correctly completes the setup to create a pipeline including PolynomialFeatures and LogisticRegression, fits it on the training data, and makes predictions?print("TP:", cm[2, 2])print("FP:", cm[1, 2])tp = cm[1, 1]fp = cm[0, 1]print("True Positives:", tp)print("False Positives:", fp)print("TP:", cm[1][1])print("FP:", cm[2][1])print("True Positives:", cm[2][2])print("False Positives:", cm[1][2])

Which evaluation metric is commonly used for binary classification problems and measures the proportion of true positive predictions among all positive examples?Select one:a. Recallb. Precision

You are evaluating a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of which 10 are incorrect.The threshold is now increased further, to 70%. Which of the following statements is TRUE?1 pointThe Recall of the classifier would Increase.The Precision of the classifier would decrease.The Recall of the classifier would increase or remain the same.The Precision of the classifier would increase or remain the same.

You are evaluating a binary classifier. There are 50 positive outcomes in the test data, and 100 observations. Using a 50% threshold, the classifier predicts 40 positive outcomes, of which 10 are incorrect.What is the classifier’s Recall on the test sample?1 point25%60%75%80%

You need to evaluate a classification model. Which metric can you use? ​​