Building Linear Regression Using Statsmodels
Building a Linear Regression Model
In statsmodels, the OLS class can be used to create a linear regression model.
We first need to initialize an OLS class object using
sm.OLS(y, X_tilde).
Then train it using the fit() method.
model = sm.OLS(y, X_tilde)
model = model.fit()
Which is equivalent to:
model = sm.OLS(y, X_tilde).fit()
The constructor of the OLS class expects a specific array X_tilde as an input, which we saw in the Normal Equation. So you need to convert your X array to X_tilde. This is achievable using the sm.add_constant() function.
Finding Parameters
When the model is trained, you can easily access the parameters using the params attribute.
123456789import statsmodels.api as sm import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv') X, y = df['Father'], df['Height'] X_tilde = sm.add_constant(X) model = sm.OLS(y, X_tilde).fit() beta_0, beta_1 = model.params print(beta_0, beta_1)
Making the Predictions
New instances can easily be predicted using predict() method, but you need to preprocess the input for them too:
12345import numpy as np X_new = np.array([65, 70, 75]) X_new_tilde = sm.add_constant(X_new) print(model.predict(X_new_tilde))
Getting the Summary
As you probably noticed, using the OLS class is not as easy as the polyfit() function. But using OLS has its benefits. While training, it calculates a lot of statistical information. You can access the information using the summary() method.
1print(model.summary())
That's a lot of statistics. We will discuss the table's most important parts in later sections.
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Building a Linear Regression Model
In statsmodels, the OLS class can be used to create a linear regression model.
We first need to initialize an OLS class object using
sm.OLS(y, X_tilde).
Then train it using the fit() method.
model = sm.OLS(y, X_tilde)
model = model.fit()
Which is equivalent to:
model = sm.OLS(y, X_tilde).fit()
The constructor of the OLS class expects a specific array X_tilde as an input, which we saw in the Normal Equation. So you need to convert your X array to X_tilde. This is achievable using the sm.add_constant() function.
Finding Parameters
When the model is trained, you can easily access the parameters using the params attribute.
123456789import statsmodels.api as sm import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b22d1166-efda-45e8-979e-6c3ecfc566fc/simple_height_data.csv') X, y = df['Father'], df['Height'] X_tilde = sm.add_constant(X) model = sm.OLS(y, X_tilde).fit() beta_0, beta_1 = model.params print(beta_0, beta_1)
Making the Predictions
New instances can easily be predicted using predict() method, but you need to preprocess the input for them too:
12345import numpy as np X_new = np.array([65, 70, 75]) X_new_tilde = sm.add_constant(X_new) print(model.predict(X_new_tilde))
Getting the Summary
As you probably noticed, using the OLS class is not as easy as the polyfit() function. But using OLS has its benefits. While training, it calculates a lot of statistical information. You can access the information using the summary() method.
1print(model.summary())
That's a lot of statistics. We will discuss the table's most important parts in later sections.
¡Gracias por tus comentarios!
