This question refers to the following code snippet, which assumes that all required libraries have been imported.Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,test_size = 0.3)yhat = GaussianNB().fit(Xtrain,ytrain).predict(Xtest)acc = accuracy_score(ytest, yhat)This code uses with of available data used for training. It outputs the based on . Every time we run this code, we will get .

Modify the code below to compute and print the accuracy.from sklearn.datasets import load_breast_cancerfrom sklearn.linear_model import LogisticRegressionfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score# Load the datasetdata = load_breast_cancer()X = data.datay = data.target# Split the data 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 the Logistic Regression modellogreg = LogisticRegression(solver='liblinear')# Train the modellogreg.fit(X_train, y_train)# Predict the test set resultsy_pred = logreg.predict(X_test)# insert code hereWhat is the accuracy of the logistic regression model on the test data?

Which of the following options will complete the missing code lines to:i) train the MLPClassifier,ii) predict the test set labels,iii) count the number of misclassified samples,iv) call the function to print the results.from sklearn.datasets import make_moonsfrom sklearn.model_selection import train_test_splitfrom sklearn.neural_network import MLPClassifierfrom sklearn.preprocessing import StandardScalerimport numpy as np# Generate a two-moon datasetX, y = make_moons(n_samples=1000, noise=0.2, 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=0.3, random_state=42)# Scale the featuresscaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test)# Initialise the MLPClassifier with one hidden layer with 10 neuronsmlp = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)# [Your Code Here] - Train the MLPClassifier on the scaled training data# [Your Code Here] - Predict the labels for the scaled test data# [Your Code Here] - Print the number of misclassified samples in the test setmlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)print(np.sum(y_test != y_pred))mlp.train(X_train_scaled, y_train)y_pred = mlp.classify(X_test_scaled)print((y_test - y_pred).count_nonzero())mlp.fit(X_train_scaled, y_train)y_pred = mlp.predict(X_test_scaled)misclassified = np.where(y_test != y_pred, 1, 0)print(misclassified.sum())mlp.train(X_train_scaled, y_train)y_pred = mlp.test(X_test_scaled)print(np.count_nonzero(y_test == y_pred))

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

Why perform fit_transform on the prediction input?

rnf=RandomForestClassifier(max_depth=4)rnf.fit(X_train,y_train)​# Building our out-of sample predictionsy_test_pred=rnf.predict(X_test)​# We can measure the out-of-sample accuracyos_accuracy=metrics.accuracy_score(y_test,y_test_pred)print('Out-of-Sample Accuracy:',round(os_accuracy,5))​# We can measure the out-of-Sample simulated investment performance os_prod=pd.Series(y_test_pred.astype('int64'),index=X_test.index).rename('Winner')retl,dial=ap.ml.analysis(os_pred.prices)dialOut-of-Sample Accuracy: 0.52622---------------------------------------------------------------------------AttributeError Traceback (most recent call last)Cell In[17], line 13 11 # We can measure the out-of-Sample simulated investment performance 12 os_prod=pd.Series(y_test_pred.astype('int64'),index=X_test.index).rename('Winner')---> 13 retl,dial=ap.ml.analysis(os_pred.prices) 14 dialAttributeError: module 'apmodule' has no attribute 'ml'