INTRODUCTION

Welcome to our blog post, where we embark on a journey to understand how the cost function in linear regression works. In this article, we will dive into the fascinating world of optimization and global minima, using a concrete numerical example to illustrate the concepts. By unraveling the inner workings of the cost function, we will demystify how it drives the process of error minimization in linear regression.

DIFFERENCE BETWEEN ERROR FUNCTION AND COST FUNCTION

NOTE:

• The terms "error function" and "cost function" are often used interchangeably, depending on the context and specific application.

• However, the key distinction lies in their primary usage: error functions focus on individual predictions, while cost functions are used to optimize models on datasets.

DIFFERENCE BETWEEN GLOBAL MINIMA AND LOCAL MINIMA

This figure gives you a clear idea.

NOTE:

• In optimization problems, finding the global minimum is typically the ultimate goal as it guarantees the best possible solution.

• However, local minima can be problematic because they can trap optimization algorithms, leading to suboptimal solutions if the global minimum is not reached.

COST FUNCTION FOR LINEAR REGRESSION

In linear regression, the most commonly used error function is the mean squared error (MSE).

$$MSE = (1/n) * Σ(y - ŷ)^2$$

where:

• n is the number of observations in the dataset,

• y represents the actual values of the target variable,

• ŷ represents the predicted values of the target variable.

WHAT IS GRADIENT DESCENT?

Gradient descent is an optimization algorithm used to minimize a function iteratively by adjusting its parameters in the direction of the negative gradient.

FORMULA

$$xᵢ₊₁ = xᵢ - α * ∇f(xᵢ)$$

where:

• xᵢ is the current parameter value at iteration i,

• α (alpha) is the learning rate that controls the step size,

• ∇f(xᵢ) is the gradient of the function f(x) concerning x, representing the direction and magnitude of the steepest descent.

WHAT IS THE LEARNING RATE?

The learning rate in gradient descent is the step size or rate at which the model parameters are adjusted during each iteration.

DERIVATION OF GRADIENT DESCENT IN LINEAR REGRESSION

W.K.T Cost function

$$MSE = (1/n) * Σ(y - ŷ)^2$$

W.K.T Equation of simple linear regression

$$ŷ=mx+c$$

Step 1: Let us take the initial point and point which is at the next step.

Equation 1: The slope between the points

$$m2=m1-L*Dm$$

Equation 2: The intercept between the points

$$c2=c1-L*Dc$$

Here,

L - Learning Rate

m1,m2 - Slope of the points

Dm - Partial derivative of cost function concerning m

Dc -Partial derivative of cost function concerning c

Step 2: Find Dm

$$∂(MSE)/∂m=∂((1/n) * Σ(y - ŷ)^2)/∂m$$

Substitute ŷ in the above equation

$$∂(MSE)/∂m=∂((1/n) * Σ(y - (mx+c))^2)/∂m$$

(a+b)^2 Formula

$$∂(MSE)/∂m=(1/n) * ∂(Σ(y^2 +(mx+c)^2-2y(mx+c))/∂m$$

Expanding

$$∂(MSE)/∂m=(1/n) * Σ(∂(y^2 +(mx)^2+c^2+2mxc-2mxy-2yc))/∂m$$

After differentiation, we get finally

$$Dm=-2/n*Σx(y-(mx+c))$$

Step 3: Find Dc

The partial derivative of MSE concerned with c

$$∂(MSE)/∂c=(1/n) * Σ(∂(y^2 +(mx)^2+c^2+2mxc-2mxy-2yc))/∂c$$

Finally, we get,

$$Dc=-2/n*Σ(y-(mx+c))$$

Step 3: Finally substitute Dm and Dc values in the equations

we get,

$$m2=m1-L*(-2/n*Σx(y-(mx+c)))$$

$$c2=c1-L*(-2/n*Σ(y-(mx+c)))$$

Step 4: This is the iterative process until it finds global minima(ideally 0) by simultaneously updating m and c for the given dataset

Hence in general,

Formulas

$$m=m+L*(2/n*Σxᵢ(yᵢ-(mxᵢ+cᵢ)))$$

$$c=c+L*(2/n*Σ(yᵢ-(mxᵢ+cᵢ)))$$

STEPS FOLLOWED IN GRADIENT DESCENT

Step1: Define the cost function

Step2: Derivative of the Cost function

Step3: Initialize lr m and c randomly

Step 4: Repeat until convergence by using the derived formula, simultaneously updating m and c.

CODE FOR GRADIENT DESCENT

#loss=(y-yhat)**2/N

import numpy as np

#initialize parameter
#for example
x=np.random.randn(10,1)
y=2*x+np.random.rand()
#initially slope and intercept
m=0.0
c=0
#let say our model leraning rate 0.1
learning_rate=0.01
#create gradient descent
def gradient_descent_linear_regression(x,y,m,c,learning_rate):
    #partial derivative
    dldm=0
    dldc=0
    #number samples in data
    N=x.shape[0]
    for xi,yi in zip(x,y):
        dldm+=-2*xi*(yi-(m*xi+c))
        dldc+=-2*(yi-(m*xi+c))

        #make update to slope and intercept
        m=m-learning_rate*(1/N)*dldm
        c=c-learning_rate*(1/N)*dldm
    return m,c

#iteratively update
for epoch in range(3):
    m,c=gradient_descent_linear_regression(x,y,m,c,learning_rate)
    yhat=m*x+c
    loss=np.divide(np.sum((y-yhat)**2),x.shape[0])
    print(f"for{epoch} Loss is {loss},slope{m},intercept{c}")

OUTPUT

for0 Loss is 3.7289331846009013,slope[0.23119888],intercept[0.23119888]
for1 Loss is 2.8466961593922298,slope[0.43515356],intercept[0.43515356]
for2 Loss is 2.2170477365170203,slope[0.61507449],intercept[0.61507449]

NOTE: For the 3 iterations in the above example for random data points we obtain

slope m=0.61

intercept=0.61 where the loss is minimum.

This is the mathematical intuition behind gradient descent in linear regression.Based on the loss it will compute the optimal slope and intercept for the data set.

CONCLUSION

Gradient descent plays a crucial role in the field of linear regression by enabling the optimization of model parameters to minimize the cost function. Through iterative updates, the algorithm guides the model toward the best-fitting line that minimizes the overall difference between predicted and actual values. By leveraging the power of gradient descent, linear regression models can effectively learn from data and make accurate predictions. Understanding and implementing gradient descent in linear regression empowers practitioners to unlock the full potential of this widely-used and essential algorithm in the realm of machine learning.

Hope you enjoyed the learning !!!

Stay tuned !!!

Thank you !!!