Python implementation of Univariate Analysis

Mathematical implementation of Univariate Optimization

We can use scipy library to implement various mathematical functions. In our implementation, we will use minimize_scalar from the scipy optimize module to find the optimum function value of a custom-defined objective function.

Python3

import numpy as np
from scipy.optimize import minimize_scalar
 
# Define the objective function
 
def objective_function(x):
    return x**2 + 3*x + 2
 
 
# Minimize the objective function
result = minimize_scalar(objective_function)
 
# Extract the optimal value 
# and corresponding function value
optimal_value = result.x
optimal_function_value = result.fun
 
# Print the results
print("Optimal value:", optimal_value)
print("Optimal function value:", optimal_function_value)

Output:

Optimal value: -1.5000000000000002
Optimal function value: -0.25

Uni-variate Optimization – Data Science

Optimization is an important part of any data science project, with the help of optimization we try to find the best parameters for our machine learning model which will give the minimum loss value. There can be several ways of minimizing the loss function, However, generally, we use variations of the gradient method for our optimization. In this article, we will discuss univariate optimization.

Tags:

#data mining #data-science #Data Mining #Machine Learning #Machine Learning

Mathematical implementation of Univariate Optimization

Python implementation of Univariate Analysis

Python3

Uni-variate Optimization – Data Science

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