Deep Learning - Crash Course 2023 - Data Standardization - 2

The video tutorial explains the process of standardizing data using the StandardScaler to achieve zero mean and unit variance. It emphasizes the importance of splitting the dataset into training and testing sets before applying the scaler to prevent information leakage. The tutorial also covers the correct method of applying the scaler: fitting and transforming the training data, then using the same scaling parameters to transform the test data. A code demonstration is provided to illustrate these concepts.

What is the primary goal of using a standard scaler on a dataset?

• To decrease the standard deviation to zero

• To achieve a zero mean and a standard deviation of one

• To double the values of the dataset

• To increase the mean of the dataset

Why is it important to split the data before applying the standard scaler?

• To ensure the test set is larger than the training set

• To prevent information leakage from the test set into the training set

• To make the training process faster

• To increase the accuracy of the model

How should the standard scaler be applied to the test dataset?

• Fit and transform the test dataset separately

• Ignore the test dataset during scaling

• Use random scaling parameters for the test dataset

• Use the scaling parameters from the training dataset to transform the test dataset

What is the first step in the practical application of the standard scaler?

• Apply the standard scaler to the entire dataset

• Visualize the dataset

• Split the data into training and test sets

• Calculate the mean of the dataset

What is the result of applying the standard scaler to the training dataset?

• The mean becomes 1.65 and the standard deviation becomes 10

• The mean becomes 1.65e17 and the standard deviation becomes one

• The mean becomes zero and the standard deviation becomes one

• The mean and standard deviation remain unchanged

What is the purpose of using the 'fit_transform' method on the training data?

• To transform the data without fitting

• To only fit the model without transforming

• To perform both fitting and transforming simultaneously

• To visualize the data

After transforming the test dataset, why might the mean and standard deviation differ from zero and one?

• Because the test dataset is larger

• Because the test dataset was not transformed

• Because the test dataset is more complex

• Because the scaling was based on the training dataset