12. from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerX_train ,xtest, ytrain, ytest = train_test_split(x,y, test_size=42) # type: ignorescaler = StandardScaler()scaler.fit(X_train) scaler.fit_transform(X_train)Which of the following statements accurately describes the difference between the fit and fit_transform methods?scaler.fit(X_train) and scaler.fit_transform(X_train) perform the same operations, both computing the mean and standard deviation as well as scaling the data.scaler.fit(X_train) applies the standard scaling transformation to X_train and returns the scaled data, while scaler.fit_transform(X_train) only computes the mean and standard deviation of X_train without scaling the data.scaler.fit(X_train) is used to both compute and apply the transformation to X_train, while scaler.fit_transform(X_train) only computes the mean and standard deviation without applying the transformation.scaler.fit(X_train) computes the mean and standard deviation of X_train and stores these statistics, but does not apply any transformation to X_train. scaler.fit_transform(X_train) also computes the mean and standard deviation of X_train, and additionally applies the transformation to X_train, returning the scaled data

12. from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerX_train ,xtest, ytrain, ytest = train_test_split(x,y, test_size=42) # type: ignorescaler = StandardScaler()scaler.fit(X_train) scaler.fit_transform(X_train)Which of the following statements accurately describes the difference between the fit and fit_transform methods?scaler.fit(X_train) and scaler.fit_transform(X_train) perform the same operations, both computing the mean and standard deviation as well as scaling the data.scaler.fit(X_train) applies the standard scaling transformation to X_train and returns the scaled data, while scaler.fit_transform(X_train) only computes the mean and standard deviation of X_train without scaling the data.scaler.fit(X_train) is used to both compute and apply the transformation to X_train, while scaler.fit_transform(X_train) only computes the mean and standard deviation without applying the transformation.scaler.fit(X_train) computes the mean and standard deviation of X_train and stores these statistics, but does not apply any transformation to X_train. scaler.fit_transform(X_train) also computes the mean and standard deviation of X_train, and additionally applies the transformation to X_train, returning the scaled data

Question 9Select the correct syntax to obtain the data split that will result in a train set that is 60% of the size of your available data.1 pointX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.6)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)X_train, y_test = train_test_split(X, y, test_size=0.40)X_train, y_test = train_test_split(X, y, test_size=0.6)

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

ValueError Traceback (most recent call last)Cell In[176], line 6 4 # Standardize features 5 scaler = StandardScaler()----> 6 X_train = scaler.fit_transform(X_train) 7 X_test = scaler.transform(X_test) 9 # Train Random Forest RegressorFile ~\anaconda3\lib\site-packages\sklearn\utils\_set_output.py:313, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs) 311 @wraps(f) 312 def wrapped(self, X, *args, **kwargs):--> 313 data_to_wrap = f(self, X, *args, **kwargs) 314 if isinstance(data_to_wrap, tuple): 315 # only wrap the first output for cross decomposition 316 return_tuple = ( 317 _wrap_data_with_container(method, data_to_wrap[0], X, self), 318 *data_to_wrap[1:], 319 )File ~\anaconda3\lib\site-packages\sklearn\base.py:1098, in TransformerMixin.fit_transform(self, X, y, **fit_params) 1083 warnings.warn( 1084 ( 1085 f"This object ({self.__class__.__name__}) has a `transform`" (...) 1093 UserWarning, 1094 ) 1096 if y is None: 1097 # fit method of arity 1 (unsupervised transformation)-> 1098 return self.fit(X, **fit_params).transform(X) 1099 else: 1100 # fit method of arity 2 (supervised transformation) 1101 return self.fit(X, y, **fit_params).transform(X)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:878, in StandardScaler.fit(self, X, y, sample_weight) 876 # Reset internal state before fitting 877 self._reset()--> 878 return self.partial_fit(X, y, sample_weight)File ~\anaconda3\lib\site-packages\sklearn\base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs) 1466 estimator._validate_params() 1468 with config_context( 1469 skip_parameter_validation=( 1470 prefer_skip_nested_validation or global_skip_validation 1471 ) 1472 ):-> 1473 return fit_method(estimator, *args, **kwargs)File ~\anaconda3\lib\site-packages\sklearn\preprocessing\_data.py:914, in StandardScaler.partial_fit(self, X, y, sample_weight) 882 """Online computation of mean and std on X for later scaling. 883 884 All of X is processed as a single batch. This is intended for cases (...)

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 .