What is the primary purpose of using the 'standard scaler' in preprocessing demographic data for clustering?To reduce the dimensionality of the dataTo convert categorical data into numerical dataTo normalise numerical data to have a mean of zero and a standard deviation of oneTo remove outliers from the dataset
Question
What is the primary purpose of using the 'standard scaler' in preprocessing demographic data for clustering?To reduce the dimensionality of the dataTo convert categorical data into numerical dataTo normalise numerical data to have a mean of zero and a standard deviation of oneTo remove outliers from the dataset
Solution
The primary purpose of using the 'standard scaler' in preprocessing demographic data for clustering is to normalise numerical data to have a mean of zero and a standard deviation of one. This is done to ensure that all features have the same scale and the algorithm does not become biased towards features with higher magnitudes.
Similar Questions
Which data scaling technique centers the data around the mean and scales it to have a standard deviation of 1?Review LaterMin-Max ScalingZ-Score StandardizationRobust ScalingLog Transformation
What is the purpose of data normalization?a. To encrypt data for securityb. To improve data retrieval efficiencyc. To remove duplicates from the datasetd. To scale data to a common format
What is Data Scaling?
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
Use StandardScaler to standardize your data
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