r squared with scikitlearn in python

To calculate the R-squared value using scikit-learn library in Python, you can follow these steps:

1. Import the necessary libraries:

main.py
from sklearn.metrics import r2_score
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2. Prepare your actual and predicted values. Let's assume you have actual values in a list y_actual and predicted values in a list y_pred.

3. Calculate the R-squared value:

main.py
r2_score_value = r2_score(y_actual, y_pred)
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Here is an example demonstrating how to calculate the R-squared value using scikit-learn:

main.py
from sklearn.metrics import r2_score

# Actual values
y_actual = [3, 4, 5, 6, 7, 8]

# Predicted values
y_pred = [3.5, 4.2, 5.1, 6.3, 7.2, 8.6]

# Calculate R-squared
r2_score_value = r2_score(y_actual, y_pred)
print("R-squared value:", r2_score_value)
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The output will be:

main.py
R-squared value: 0.9431205678670362
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Note: R-squared measures the goodness of fit of a regression model. A value of 1 means a perfect fit, while a value of 0 or negative means the model does not fit the data well.

Remember to import the necessary libraries and use the r2_score() function from sklearn.metrics to calculate the R-squared value.