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])

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

Which of the following techniques could allow Google’s Gmail to identify spam or irrelevant emails in the inbox?ClassificationRegression Traditional ProgrammingClustering

What is the value of True Positive (TP) in the confusion matrix generated by the RandomForestClassifier below? Modify the code to print the value.from sklearn.metrics import confusion_matrixfrom sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifier# Generate synthetic binary classification datasetX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the dataset into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise and train the RandomForestClassifierrf_classifier = RandomForestClassifier(random_state=42)rf_classifier.fit(X_train, y_train)# Predict the test set resultsy_pred = rf_classifier.predict(X_test)# Generate the confusion matrixcm = confusion_matrix(y_test, y_pred)# insert code here

Suppose you are analysing the performance of a new email spam detection system using precision and recall. You have already computed these metrics, and you are about to explore their trade-offs to optimise the classifier's threshold. Given the code snippet below, identify the correct function call that would allow you to adjust and visualise the precision-recall trade-off.from sklearn.metrics import precision_recall_curveimport matplotlib.pyplot as pltfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import make_classification# Generate synthetic data for binary classificationX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Train a RandomForest classifierclassifier = RandomForestClassifier(random_state=42)classifier.fit(X_train, y_train)# Predict probabilities for the test sety_scores = classifier.predict_proba(X_test)[:, 1]# [Your Code Here] - Generate precision and recall values for various thresholdsplt.plot(precision_recall_curve(y_test, y_scores))precision, recall, thresholds = precision_recall_curve(y_test, y_scores)precision_recall_curve(classifier, X_test, y_test)precision, recall = precision_recall_curve(y_test, y_scores)

_________also known as junk email—is a type of electronic spam where unsolicited messages are sent by email.*1 pointHackingSpamFiltered messagesGmail messages