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

For the given confusion matrix, compute the recall True Positive True NegativePredicted Positive 8 3Predicted Negative 2 7 0.73 0.7 0.78 0.8

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])

The confusion matrix highlights a problem of the kNN classifier as it is used now. Can you find it and explain why?

Consider a classification problem with three classes: A, B, and C. A machine learning model is trained on a labeled dataset, and the confusion matrix for the model's predictions is given below:What is the overall accuracy of the model?a)0.69b)0.85c)0.8d)0.725

The default value of test_size parameter in train_test_split() is _____.1 point0.250.20.80.32. The confusion_matrix() function comes under _____ module.1 pointsklearn.utilssklearn.metricssklearn.model_selectionsklearn.calibration3. Pandas ______ is used to view some basic statistical details like percentile, mean, std etc. of a data frame.1 pointdescribe()desc()details()info()4. Consider a dataframe df containg two tuples. Then df.head() will return1 pointFive tuples where bottom 3 containing NoneFive tuples where bottom 3 containing garbage valuesTwo tuplesError5. To select a specific column (say ‘col3’) from a dataframe (say ‘df’), we have to write1 pointdf(‘col3’)df[['col3']]df.col3df[3]6. To implement linear regression, we can use _____.1 pointsklearn.model_selection.LinearRegression()sklearn.multiclass.LinearRegression()sklearn.preprocessing.LinearRegression()sklearn.linear_model.LinearRegression()7. What is the effect of following line:                                                 df = df.dropna(axis=0)1 pointDrops all rowsDrops all columnsDrop rows with null valuesDrop columns with null values8. Following data points represents ___________.1 pointPositive CorrelationNegative CorrelationNegative CovarianceZero Covariance9. Regression is one of the types of supervised learning models, where data is classified according to labels and output data need not be continuous. (True/False)1 pointTrueFalse10. Which of the following is defined as the measure of balance between precision and recall?1 pointAccuracyF1-scoreReliabilityPunctuality11. _____ helps to find the best model that represents our data and how well the chosen model will work in future.1 pointEvaluationPerformance MeasureLearningValidation12. While evaluating a model's performance, recall parameter considers _____.1 pointFalse PositiveFalse NegativeTrue PositiveTrue Negative13. Two conditions when prediction matches with the reality are true positive and __________.1 pointFalse PositiveFalse NegativeTrue PositiveTrue Negative14. Odd man out:Regression, Classification, Clustering1 pointRegressionClassificationClustering15. Which of the following talks about how true the predictions are by any model?1 pointAccuracyReliablityRecallF1-score16. Which of the following tasks can be best solved using reinforcement learning?1 pointPredicting the amount of rainfall based on various cuesDetecting fraudulent credit card transactionsTraining a robot to solve a maze17. During linear regression, with regard to residuals, which among the following is true?1 pointLower is betterHigher is betterDepends upon the dataNone of the above18. We can handle missing values in Machine Learning by1 pointDeleting rows with missing valuesReplacing with the mean, median, or mode of remaining values in the columnReplacing with the most frequent categoryAll of the mentioned19. Which of the following is NOT supervised learning?1 pointPCADecision TreeLinear RegressionNaive Bayesian20. A computer program is said to learn if1 pointIt improves with experienceIt learns from experienceIt learns from mistakesIt learns from supervisor21. A well-defined learning problem must include1 pointTaskPerformance measureTraining experienceAll of the mentioned22. Inductive bias is the assumption made by the learner.1 pointTrueFalse23. If X represents a matrix of feature, then1 pointA row in the X represents one data point or one instanceA column in the X represents one feature or one attributeAll of the mentionedNone of the mentioned24. Semi-supervised Learning combines a __________ with a __________ during training.1 pointsmall amount of labelled data, large amount of unlabelled datasmall amount of labelled data, small amount of unlabelled datalarge amount of labelled data, large amount of unlabelled datalarge amount of labelled data, small amount of unlabelled data25. In multiple regression, we have ____ independent variable and _____ dependent variable.1 pointsingle, singlemore than one, singlemore than one, more than onesingle, more than one26.  Entropy([9+,5-]) = ?1 point0.2460.2830.940.6527.  Entropy([5+,0-]) = ?1 point0.50.25010.7528. To measure the overall strength of the model in regression analysis, we use _______.1 pointFactor analysisCoefficient of partial correlationCoefficient of partial regressionCoefficient of determination29.  What is the purpose of performing cross-validation?1 pointTo assess the predictive performance of the modelsTo judge how the trained model performs outside the sample on test dataAll of the mentionedNone of the above30. What does p indicate in the following figure?1 pointProportionProbabilityPrecisionPercentage