updated statsmodels import statement

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edit approved Mar 25, 2022 at 18:21

3
1

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference?

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference?

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference?

removed 'thanks', tidied up title

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edit approved Nov 22, 2021 at 4:52

desertnaut

286
2
10

Wildly different $R^2$ between statsmodels linear regression in statsmodels and sklearn linear regression

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference? Thanks so much!

Wildly different $R^2$ between statsmodels linear regression and sklearn linear regression

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference? Thanks so much!

Wildly different $R^2$ between linear regression in statsmodels and sklearn

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference?

replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/

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edited Apr 13, 2017 at 12:44

Community Bot

1

My question is related to:

Difference between statsmodel OLS and scikit linear regression Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference? Thanks so much!

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference? Thanks so much!

My question is related to:

Difference between statsmodel OLS and scikit linear regression

I essentially have the same problem, except my results are even more substantially different. Performing the following simple linear regressions, I get almost completely opposite results for the coefficient of determination:

import statsmodels.formula.api as sm
from sklearn import linear_model

    x1 = [26.0, 31.0, 47.0, 51.0, 50.0, 49.0, 37.0, 33.0, 49.0, 54.0, 31.0, 49.0, 48.0, 49.0, 49.0, 47.0, 44.0, 48.0, 35.0, 43.0]
    y1 = [116.0, 94.0, 100.0, 102.0, 116.0, 116.0, 68.0, 118.0, 91.0, 104.0, 78.0, 116.0, 90.0, 109.0, 116.0, 118.0, 108.0, 119.0, 110.0, 102.0]

# Fit and summarize statsmodel OLS model
model_sm = sm.OLS(x1, y1)
result_sm = model_sm.fit()
print(result_sm.summary())


# Create sklearn linear regression object
ols_sk = linear_model.LinearRegression(fit_intercept=True)

# fit model
model_sk = ols_sk.fit(pd.DataFrame(x1), pd.DataFrame(y1))

# sklearn coefficient of determination
coefofdet = model_sk.score(pd.DataFrame(x1), pd.DataFrame(y1))

print('sklearn R^2: ' + str(coefofdet))

Statsmodels give me an $R^2$ of 0.962, while sklearn gives me an $R^2$ of 0.0584069073664.

What is causing such a drastic difference? Thanks so much!

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asked Dec 6, 2016 at 0:50

Terrence J

215
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Wildly different $R^2$ between statsmodels linear regression in statsmodels and sklearn linear regression

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