Using Python, Create a k-fold cross validation code with the number of folds being 10. The raw data needs to be split into training and testing. The number of folds is the number of times you should go through a for loop. Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds) Do normal linear regression model fitting Keep track of the R^2 scores of the folds Print the average and standard deviation of the R^2 value for the different folds
Using Python, Create a k-fold cross validation code with the number of folds being 10. The raw data needs to be split into training and testing. The number of folds is the number of times you should go through a for loop. Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds) Do normal linear regression model fitting Keep track of the R^2 scores of the folds Print the average and standard deviation of the R^2 value for the different folds
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Using Python,
Create a k-fold cross validation code with the number of folds being 10.
- The raw data needs to be split into training and testing.
- The number of folds is the number of times you should go through a for loop.
- Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds)
- Do normal linear regression model fitting
- Keep track of the R^2 scores of the folds
- Print the average and standard deviation of the R^2 value for the different folds
![Our libraries that we are importing:
# 1a. Importing all the libraries necessary for this assignment
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
[7] # 1b. Loading the data
data = sns.load_dataset('geyser')
Let's check out this dataset.
[9] print('
data
1
duration waiting kind
3.600
79
long
1
1.800
54 short
2
3.333
74
long
3
2.283
62 short
4
4.533
85
long
267
4.117
81
long
268
2.150
46 short
269
4.417
90
long
270
1.817
46 short
271
4.467
74
long
272 rows x 3 columns
Imagine working at Yellowstone National Park as a scientist researching Old Faithful Geyser. The boss wants us to evaluate the predictiveness
of the relationship between duration of an eruption in minutes ("duration") and the duration in minutes until the next eruption ("waiting"). We
collected the data, but the internet in Yellowstone is not very good, so we cannot access the crossvalidation library in python](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7c9fe6be-99fb-4863-a04f-7bda979173e6%2F8224c764-6eda-4fff-b719-d20b5a73993e%2Fbejud5m_processed.png&w=3840&q=75)
Transcribed Image Text:Our libraries that we are importing:
# 1a. Importing all the libraries necessary for this assignment
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
[7] # 1b. Loading the data
data = sns.load_dataset('geyser')
Let's check out this dataset.
[9] print('
data
1
duration waiting kind
3.600
79
long
1
1.800
54 short
2
3.333
74
long
3
2.283
62 short
4
4.533
85
long
267
4.117
81
long
268
2.150
46 short
269
4.417
90
long
270
1.817
46 short
271
4.467
74
long
272 rows x 3 columns
Imagine working at Yellowstone National Park as a scientist researching Old Faithful Geyser. The boss wants us to evaluate the predictiveness
of the relationship between duration of an eruption in minutes ("duration") and the duration in minutes until the next eruption ("waiting"). We
collected the data, but the internet in Yellowstone is not very good, so we cannot access the crossvalidation library in python
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