In the previous lesson, you looked at the Coefficient of Determination, what it means, and how it is calculated. In this lesson, you'll use the R-Squared formula to calculate it in Python and NumPy.

You will be able to:

• Calculate the coefficient of determination using self-constructed functions
• Use the coefficient of determination to determine model performance

Once a regression model is created, we need to decide how "accurate" the regression line is to some degree.

Here is the equation for R-Squared or the Coefficient of Determination again:

$$ R^2 = 1- \dfrac{SS_{RES}}{SS_{TOT}} = 1- \dfrac{\sum_i(y_i - \hat y_i)^2}{\sum_i(y_i - \overline y_i)^2} $$

Note that this is also equal to:

$$ R^2 = 1- \dfrac{SS_{RES}}{SS_{TOT}}=\dfrac{SS_{EXP}}{SS_{TOT}} $$ where

• $SS_{TOT} = \sum_i(y_i - \overline y_i)^2$ $\rightarrow$ Total Sum of Squares
• $SS_{EXP} = \sum_i(\hat y_i - \overline y_i)^2$ $\rightarrow$ Explained Sum of Squares
• $SS_{RES}= \sum_i(y_i - \hat y_i)^2 $ $\rightarrow$ Residual Sum of Squares

Recall that the objective of $R^2$ is to learn how much of the error is a result in variation in the data features, as opposed to being a result of the regression line being a poor fit.

Let's calculate R-Squared in Python. The first step would be to calculate the Squared Error. Remember that the Squared Error is the Residual Sum of Squares of the difference between a given line and the actual data points.

Create a function sq_err() that takes in y points for 2 arrays, calculates the difference between corresponding elements of these arrays, squares the differences, and sums all the squared differences. The function should return the RSS value you saw earlier.

# Calculate sum of squared errors between regression and mean line 
import numpy as np

def sq_err(y_real, y_predicted):
    """
    input
    y_real : true y values
    y_predicted : regression line

    
    return
    squared error between regression and true line (ss_tot)
    """
    pass

# Check the output with some example data
Y = np.array([1, 3, 5, 7])
Y_pred = np.array([4.1466666666666665, 2.386666666666667, 3.56, 5.906666666666666])

sq_err(Y, Y_pred)

# 13.55

Squared error, as calculated above is only a part of the coefficient of determination. Let's now build a function that uses the sq_err() function above to calculate the value of R-Squared by first calculating SSE, then use this same function to calculate SST (use the mean of $y$ instead of the regression line), and then plug in these values into the R-Squared formula. Perform the following tasks

• Calculate the mean of the y_real
• Calculate SSR using sq_err() or SSE
• Calculate SST
• Calculate R-Squared from above values using the given formula

# Calculate Y_mean , squared error for regression and mean line , and calculate r-squared

def r_squared(y_real, y_predicted):
    """
    input
    y_real: real values
    y_predicted: regression values
    
    return
    r_squared value
    """
    pass

# Check the output with some example data
Y = np.array([1, 3, 5, 7])
Y_pred = np.array([4.1466666666666665, 2.386666666666667, 3.56, 5.906666666666666])

r_squared(Y, Y_pred)

# 0.32

This R-Squared value is very low, but remember that it wasn't from real data. So now, we have quite a few functions for calculating slope, intercept, best-fit line, plotting and calculating R-squared. In the next lab, you'll put these all together to run a complete regression experiment.

In this lesson, you learned how to calculate the R-Squared using Python and NumPy. In the next lab, you will put all the functions from the last few labs together to create a complete DIY regression experiment.