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Non linear Regression examples – ML
  • Last Updated : 17 Jul, 2020

Non-Linear regression is a type of polynomial regression. It is a method to model a non-linear relationship between the dependent and independent variables. It is used in place when the data shows a curvy trend, and linear regression would not produce very accurate results when compared to non-linear regression. This is because in linear regression it is pre-assumed that the data is linear.

Code:




import numpy as np
import pandas as pd
  
# downloading dataset 
! wget -nv -O china_gdp.csv https://s3-api.us-geo.objectstorage.softlayer.net/
                    cf-courses-data/CognitiveClass/ML0101ENv3/labs/china_gdp.csv
      
df = pd.read_csv("china_gdp.csv")
  
def sigmoid(x, Beta_1, Beta_2):
     y = 1 / (1 + np.exp(-Beta_1*(x-Beta_2)))
     return y
      
beta_1 = 0.10
beta_2 = 1990.0
  
# logistic function
Y_pred = sigmoid(x_data, beta_1, beta_2)
  
# plot initial prediction against datapoints
plt.plot(x_data, Y_pred * 15000000000000.)
plt.plot(x_data, y_data, 'ro')    

The scatter plot shows the relationship between GDP and time of a country, but the relationship is not linear. Instead after 2005 the line starts to become curve and does not follow a linear straight path. In such cases, a special estimation method is required called the non-linear regression.

Code:






import numpy as np
import pandas as pd
  
# downloading dataset 
! wget -nv -O china_gdp.csv https://s3-api.us-geo.objectstorage.softlayer.net/ 
         cf-courses-data / CognitiveClass / ML0101ENv3 / labs / china_gdp.csv
      
df = pd.read_csv("china_gdp.csv")
  
def sigmoid(x, Beta_1, Beta_2):
     y = 1 / (1 + np.exp(-Beta_1*(x-Beta_2)))
     return y
       
x = np.linspace(1960, 2015, 55)
x = x / max(x)
y = sigmoid(x, *popt)
  
plt.figure(figsize =(8, 5))
plt.plot(xdata, ydata, 'ro', label ='data')
plt.plot(x, y, linewidth = 3.0, label ='fit')
plt.legend(loc ='best')
plt.ylabel('GDP')
plt.xlabel('Year')
plt.show()

Output:



There are many different regressions that exists and can be used to fit whatever the dataset looks like such as quadratic, cubic regression, and so on to infinite degrees according to our requirement.

Code:




import numpy as np
import matplotlib.pyplot as plt % matplotlib inline
  
x = np.arange(-5.0, 5.0, 0.1)
  
## You can adjust the slope and intercept to verify the changes in the graph
y = 2*(x) + 3
y_noise = 2 * np.random.normal(size = x.size)
ydata = y + y_noise
# plt.figure(figsize =(8, 6))
plt.plot(x, ydata,  'bo')
plt.plot(x, y, 'r'
plt.ylabel('Dependent Variable')
plt.xlabel('Indepdendent Variable')
plt.show()

Output:

Code:




import numpy as np
import matplotlib.pyplot as plt % matplotlib inline
  
x = np.arange(-5.0, 5.0, 0.1)
  
## You can adjust the slope and intercept to verify the changes in the graph
  
y = np.power(x, 2)
y_noise = 2 * np.random.normal(size = x.size)
ydata = y + y_noise
plt.plot(x, ydata,  'bo')
plt.plot(x, y, 'r'
plt.ylabel('Dependent Variable')
plt.xlabel('Indepdendent Variable')
plt.show()

Output:

Quadratic Regression

Code:




import numpy as np
import matplotlib.pyplot as plt % matplotlib inline
  
x = np.arange(-5.0, 5.0, 0.1)
  
## You can adjust the slope and intercept to verify the changes in the graph
y = 1*(x**3) + 1*(x**2) + 1 * x + 3
y_noise = 20 * np.random.normal(size = x.size)
ydata = y + y_noise
plt.plot(x, ydata,  'bo')
plt.plot(x, y, 'r'
plt.ylabel('Dependent Variable')
plt.xlabel('Indepdendent Variable')
plt.show()

Output:

Cubic Regression


We can call all of these polynomial regression, where the relationship between the independent variable X and the dependent variable Y is modeled as an Nth degree polynomial in X.

Polynomial Regression


For a model to be considered non-linear, Y hat must be a non-linear function of the parameters Theta, not necessarily the features X. When it comes to non-linear equation, it can be the shape of exponential, logarithmic, and logistic, or many other types.
Output:

As you can see in all of these equations, the change of Y hat depends on changes in the parameters Theta, not necessarily on X only. That is, in non-linear regression, a model is non-linear by parameters.

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