Consider the following code snippet: X = [[1, 2], [2, 3], [3, 4], [5, 6], [7, 8]] scaler = StandardScaler()X_scaled = scaler.fit_transform(X)model = AgglomerativeClustering(n_clusters=2, linkage='average')model.fit(X_scaled) Why do we use the fit_transform() method to scale the data?To increase the size of the datasetTo reduce the number of features in the datasetTo assign cluster labels to each data pointTo ensure each feature contributes equally to the distance calculations

What is the purpose of feature scaling in machine learning?Question 10Answera.To remove outliers from the datab.To standardize the range of featuresc.To increase the complexity of modelsd.To decrease the dimensionality of features

The main purpose of scaling features before fitting a k nearest neighbor model is to:1 pointBreak ties in case there is the same number of neighbors of different classes next to a given observationEnsure decision boundaries have roughly the same size for all classesEnsure that features have similar influence on the distance calculationHelp find the appropriate value of k

What does the following line of code achieve? centers = scaler.inverse_transform(km.cluster_centers_)where km = KMeans(n_clusters=K, random_state=42)scaler = StandardScaler()It predicts the cluster centres for the given dataIt reduces the dimensionality of the cluster centresIt scales the cluster centres to have a mean of 0 and a standard deviation of 1It transforms the cluster centres back to the original feature space

What is Data Scaling?

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