Linear Regression

1. Regression Model

In statistics, linear regression is a linear approach to modelling the relationship between a dependent variable and one or more independent variables. Let x1x_1 be the independent variable and yy be the dependent variable. We will define a linear relationship between these two variables as follows:

y=θ0+θ1x1y = \theta_0+\theta_1 x_1

2. Define Loss Function

We will use the Mean Squared Error function.

L=1ni=1n(ytrueypredicted)2L = \frac{1}{n}\sum_{i=1}^n (y_{true}-y_{predicted})^2

3. Utilize the Gradient Descent Algorithm

You might know that the partial derivative of a function at its minimum value is equal to 0. So gradient descent basically uses this concept to estimate the parameters or weights of our model by minimizing the loss function.

  1. Initialize the weights, θ0=0\theta_0 = 0and θ1=0\theta_1 =0

  2. Calculate the partial derivatives w.r.t. to θ0\theta_0and θ1\theta_1 dθ0=2ni=1n(yiyiˉ)dθ1=2ni=1n(yiyiˉ)×xid_{\theta_0} = -\frac{2}{n} \sum_{i=1}^n(y_i - \bar{y_i}) \\ d_{\theta_1} = -\frac{2}{n} \sum_{i=1}^n(y_i - \bar{y_i}) \times x_i

  3. Update the weights θ0=θ0l×dθ0θ1=θ1l×dθ1\theta_0 = \theta_0 - l \times d_{\theta_0} \\ \theta_1 = \theta_1 - l \times d_{\theta_1}

Python Implementation

# Importing libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
# Preparing the dataset
data = pd.DataFrame({'feature' : [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], 'label' : [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30]})
# Divide the data to training set and test set
X_train, X_test, y_train, y_test = train_test_split(data['feature'], data['label'], test_size=0.30)
# Method to make predictions
def predict(X, theta0, theta1):
# Here the predict function is: theta0+theta1*x
return np.array([(theta0 + theta1*x) for x in X])
def linear_regression(X, Y):
# Initializing variables
theta0 = 0
theta1 = 0
learning_rate = 0.001
epochs = 300
n = len(X)
# Training iteration
for epoch in range(epochs):
y_pred = predict(X, theta0, theta1)
## Here the loss function is: 1/n*sum(y-y_pred)^2 a.k.a mean squared error (mse)
# Derivative of loss w.r.t. theta0
theta0_d = -(2/n) * sum(Y-y_pred)
# Derivative of loss w.r.t. theta1
theta1_d = -(2/n) * sum(X*(Y-y_pred))
theta0 = theta0 - learning_rate * theta0_d
theta1 = theta1 - learning_rate * theta1_d
return theta0, theta1
# Training the model
theta0, theta1 = linear_regression(X_train, y_train)
# Making predictions
y_pred = predict(X_test, theta0, theta1)
# Evaluating the model