Regression – Learn R Programming with B Tech Colleges
Develop the skills in Machine Learning with R Programming
R Programming is a language and software environment for graphics representation, statistical analysis, and reporting. However, it is widely used by the students of B Tech Colleges. Let’s perform simple strategies to solve your programming issues. We can do this by using Python. Firstly, we will calculate the squared error.
def squared_error(ys_orig,ys_line):
return sum((ys_line - ys_orig) * (ys_line - ys_orig))
The above function allows students of Computer Science Engineering at B Tech College in Jaipur to calculate the squared error of any line to data points. This sort of syntax can be used for both the regression line and the mean of the ys. In addition, the squared error is only a part of the coefficient of determination; it helps to build the function instead. Since the squared error function is only one line, aspirants could elect to have it just be a line within the coefficient of determination function. But the squared error is used outside of this function. Therefore, you can use it as your own functions. For r squared:
def coefficient_of_determination(ys_orig,ys_line):
y_mean_line = [mean(ys_orig) for y in ys_orig]
squared_error_regr = squared_error(ys_orig, ys_line)
squared_error_y_mean = squared_error(ys_orig, y_mean_line)
return 1 - (squared_error_regr/squared_error_y_mean)
How square error can be concluded?
Here, we have calculated the "y" mean line for the students of Top Engineering Colleges in Jaipur by making use of 1 liner for the loop. In addition, the square error is calculated from the "y' mean along with the regression line that uses the above function later. What we are doing here is to calculate the y mean line, using a 1 liner for a loop. Similarly, we will calculate the r squared value, which is simply 1 minus the regression line's squared error divided by the "y" mean line's squared error. In conclusion, we return the value.
from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
xs = np.array([1,2,3,4,5], dtype=np.float64)
ys = np.array([5,4,6,5,6], dtype=np.float64)
def best_fit_slope_and_intercept(xs,ys):
m = (((mean(xs)*mean(ys)) - mean(xs*ys)) /((mean(xs)*mean(xs)) - mean(xs*xs)))
b = mean(ys) - m*mean(xs) return m, b
def squared_error(ys_orig,ys_line):
return sum((ys_line - ys_orig) * (ys_line - ys_orig))
def coefficient_of_determination(ys_orig,ys_line):
y_mean_line = [mean(ys_orig) for y in ys_orig]
squared_error_regr = squared_error(ys_orig, ys_line)
squared_error_y_mean = squared_error(ys_orig, y_mean_line)
return 1 - (squared_error_regr/squared_error_y_mean)
m, b = best_fit_slope_and_intercept(xs,ys)
regression_line = [(m*x)+b for x in xs]
r_squared = coefficient_of_determination(ys,regression_line)
print(r_squared)
##plt.scatter(xs,ys,color='#003F72',label='data')
##plt.plot(xs, regression_line, label='regression line')
##plt.legend(loc=4)
##plt.show()
Output: 0.321428571429
That is a very low value. Therefore, the best-fit line isn't all that great according to this measure. Is R square a good measure in this case? However, it may depend on what goals the students of Engineering Colleges in Jaipur have. In most cases, if you care about predicting exact future values, R square is indeed very useful.
What is the role of Algorithm in the equation?
The algorithms are very basic. It does not leave any room for error, but, later on, you are likely to have layers upon layers. Not just hierarchical layers for the algorithm itself to consider, but the algorithm will be compromised of many layers of algorithms. However, students of Best Engineering Colleges in Rajasthan need to test these to make sure our assumptions about how these algorithms are meant to act are true. Consider how simple it would be to screw up the order of operations in a function, and then, disrupt the entire validity of thousands of lines of code after that.
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