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

Do all features need to be scaled when using machine learning algorithms?

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

Problem statementSend feedbackYou have been provided with a customer dataset:Why do you think there is a need to do feature scaling in this dataset?

hich of the following(s) is/are feature scaling techniques?

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