Question

Suppose the shape of a given data is (100, 15). Our task is to perform regression on the last 5 columns using others 10. We assigned 20%
of this data for validation while training process, 20% for evaluating the model after it is trained. What are the correct dimensions of
x_train, y_train, x_valid, y_valid, x_test, y_test respectively?
(60, 5), (60, 10), (20, 5), (20, 10), (20, 5), (20, 10)
(20, 5), (20, 10), (20, 10), (20, 5), (80, 5), (80, 10)
(60, 10), (60, 5), (20, 10), (20, 5), (20, 10), (20, 5)
(20, 10), (20, 5), (20, 10), (20, 5), (80, 10), (80, 5)
(80, 5), (80, 10), (0, 0), (0, 0), (20, 5), (20, 10)
(60, 10), (60, 5), (0, 0), (0, 0), (40, 10), (40, 5)

Answer 1

• The correct dimensions of x_train, y_train, x_valid, y_valid, x_test, y_test is (60, 10), (60, 5), (20, 10), (20, 5), (20, 10), (20, 5)
• In Supervised learning the three tasks are training, validation and testing.
• From the question 20% of dataset is used for validation and 20% for testing. Hence remaining 60% is used for training
• The total dataset has 100 rows and 15 columns i.e., (100,15).
• Since last 5 columns are used to perform regression using other 10 columns. the input vector "x" is of size (1,10) and output vector "y" is of the size (1,5)
• The number of rows in the dataset is 100. For training 60% (60 rows) are used. For validation 20% (20 rows) are used. For testing 20% (20 rows are used.  
• Hence the sizes of x_train is (60, 10); y_train is (60, 5); x_valid is (20, 10); y_valid is (20, 5); x_test is (20, 10) ; y_test is (20, 5)

Answer 2

Answer:

The correct dimensions of x_train, y_train, x_valid, y_valid, x_test, y_test is (60, 10), (60, 5), (20, 10), (20, 5), (20, 10), (20, 5)

In Supervised learning the three tasks are training, validation and testing.

From the question 20% of dataset is used for validation and 20% for testing. Hence remaining 60% is used for training

The total dataset has 100 rows and 15 columns i.e., (100,15).

Since last 5 columns are used to perform regression using other 10 columns. the input vector "x" is of size (1,10) and output vector "y" is of the size (1,5)

The number of rows in the dataset is 100. For training 60% (60 rows) are used. For validation 20% (20 rows) are used. For testing 20% (20 rows are used.  

Hence the sizes of x_train is (60, 10); y_train is (60, 5); x_valid is (20, 10); y_valid is (20, 5); x_test is (20, 10) ; y_test is (20, 5)

Explanation: