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
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
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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Question
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
In this tutorial, we will utilize `sklearn`'s `train_test_split()` function to define our train and test sets. The train data (predictors) will be stored in `MR_train_X` and the labels (outcomes) in `MR_train_Y`. Similarly, test data will be stored in `MR_test_X` and test labels will be stored in `MR_test_Y`.
To achieve this, we will provide the predictors (`MR_X`) and outcomes (`MR_Y`) to the function using these specified arguments:
- **test_size:** 0.2
- **random_state:** 200
```python
# 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)
```
Now, let's verify the shape of the train and test sets to ensure they have been split correctly.
```python
assert MR_train_X.shape[0] == MR_train_Y.shape[0]
assert MR_test_X.shape[0] == MR_test_Y.shape[0]
assert len(MR_train_X) == 4000
assert len(MR_test_Y) == 1000
```
#### Error Explanation
This code includes an error trace:
```
AssertionError
```
This indicates an `AssertionError`, specifying there might be a mismatch in the expected dimensions or the number of samples between train and test sets. Further investigation is required to rectify this discrepancy.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffc05db72-f983-4375-96ae-957cd35eb6ec%2F20e97627-b341-40a0-9cf8-03aa0f9a20f4%2Fq35rsdj_processed.png&w=3840&q=75)
Transcribed Image Text:### 1g) Defining the Train & Test Sets
In this tutorial, we will utilize `sklearn`'s `train_test_split()` function to define our train and test sets. The train data (predictors) will be stored in `MR_train_X` and the labels (outcomes) in `MR_train_Y`. Similarly, test data will be stored in `MR_test_X` and test labels will be stored in `MR_test_Y`.
To achieve this, we will provide the predictors (`MR_X`) and outcomes (`MR_Y`) to the function using these specified arguments:
- **test_size:** 0.2
- **random_state:** 200
```python
# 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)
```
Now, let's verify the shape of the train and test sets to ensure they have been split correctly.
```python
assert MR_train_X.shape[0] == MR_train_Y.shape[0]
assert MR_test_X.shape[0] == MR_test_Y.shape[0]
assert len(MR_train_X) == 4000
assert len(MR_test_Y) == 1000
```
#### Error Explanation
This code includes an error trace:
```
AssertionError
```
This indicates an `AssertionError`, specifying there might be a mismatch in the expected dimensions or the number of samples between train and test sets. Further investigation is required to rectify this discrepancy.
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