Answered: Now, we'll instead use sklearn's…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Now, we'll instead use sklearn's train_test_split() function here to define our train and test set. Store train data (predictors) into MR_train_X and labels (outcomes) into MR_train_Y. Similarly, store test data into MR_test_X and test labels into MR_test_Y.

In addition to providing the predictors (MR_X) and outcomes (MR_Y) to the function, we will use the following arguments for this task:

test_size: 0.2
random_state: 200

1g) Defining the train & test sets
Now, we'll instead use sklearn 's train_test_split() function here to define our train and test set. Store train data (predictors) into MR_train_X
and labels (outcomes) into MR_train_Y . Similarly, store test data into MR_test_x and test labels into MR_test_Y.
In addition to providing the predictors ( MR_X ) and outcomes ( MR_Y ) to the function, we will use the following arguments for this task:
• test_size: 0.2
random_state : 200
In [31]: #your code here
from sklearn.model_selection import train_test_split
MR_train_X, MR_train_Y, MR_test_X, MR_test_Y = train_test_split (MR_X, MR_Y, test_size=0.2, random_state=200)
In [32] assert MR_train_X.shape [0]
assert MR_test_X.shape [0]
AssertionError
==
MR_train_Y.shape [0]
MR_test_Y.shape [0]
==
assert len (MR_train_X) == 4000
assert len (MR_test_Y)
== 1000
AssertionError:
Traceback (most recent call last)
/tmp/ipykernel_191/1579675920.py in <module>
----> 1 assert MR_train_X.shape [0]
2 assert MR_test_X.shape [0]
3
4 assert len (MR_train_X) == 4000
5 assert len (MR_test_Y) == 1000
MR_train_Y.shape [0]
MR_test_Y.shape [0]

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