What is weighted least squares regression in ML python
This recipe explains what is weighted least squares regression in ML python
Recipe Objective.
What is weighted least squares regression? How to perform it in python?
Weighted least squares regression is accustomed to correct for heteroscedasticity. During a Weighted regression procedure additional weight is given to the observations with smaller variance as a result of these observations give additional reliable info concerning the regression perform than those with massive variances.
Table of Contents
Step 1- Importing Libraries.
import pandas as pd
import statsmodels.api as sm
Step 2- Reading Dataset.
df= pd.read_csv('/content/sample_data/california_housing_train.csv')
df.head()
Step 3- Spliting the data.
We have to split the data in X and Y to fit it in the wls model.
Y=df['median_house_value']
X=df.drop(['median_house_value'], axis=1)
Step 4- Fitting the model
We will fit the dataset into the model and print the summary.
wls_model = sm.WLS(Y,X)
results = wls_model.fit()
print(results.summary())
If the weights square measure a operate of the info, then the post estimation statistics like fvalue and mse_model may not be correct, because the package doesn't nonetheless support no-constant regression.
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