How to get a regression summary in scikit-learn like R does?

Question

As an R user, I wanted to also get up to speed on scikit.

Creating a linear regression model(s) is fine, but can’t seem to find a reasonable way to get a standard summary of regression output.

Code example:

# Linear Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LinearRegression

# Load the diabetes datasets
dataset = datasets.load_diabetes()

# Fit a linear regression model to the data
model = LinearRegression()
model.fit(dataset.data, dataset.target)
print(model)

# Make predictions
expected = dataset.target
predicted = model.predict(dataset.data)

# Summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print model.intercept_, model.coef_, mse, 
print(model.score(dataset.data, dataset.target))

Issues:

seems like the intercept and coef are built into the model, and I just type print (second to last line) to see them.
What about all the other standard regression output like R^2, adjusted R^2, p values, etc. If I read the examples correctly, seems like you have to write a function/equation for each of these and then print it.
So, is there no standard summary output for lin. reg. models?
Also, in my printed array of outputs of coefficients, there are no variable names associated with each of these? I just get the numeric array. Is there a way to print these where I get an output of the coefficients and the variable they go with?

My printed output:

LinearRegression(copy_X=True, fit_intercept=True, normalize=False)
152.133484163 [ -10.01219782 -239.81908937  519.83978679  324.39042769 -792.18416163
  476.74583782  101.04457032  177.06417623  751.27932109   67.62538639] 2859.69039877
0.517749425413

Notes: Started off with Linear, Ridge and Lasso. I have gone through the examples. Below is for the basic OLS.

Asked By: mpg

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Source

Answer 1

There exists no R type regression summary report in sklearn. The main reason is that sklearn is used for predictive modelling / machine learning and the evaluation criteria are based on performance on previously unseen data (such as predictive r^2 for regression).

There does exist a summary function for classification called sklearn.metrics.classification_report which calculates several types of (predictive) scores on a classification model.

For a more classic statistical approach, take a look at statsmodels.

Answered By: eickenberg

Answer 2

Use model.summary() after predict

# Linear Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LinearRegression

# load the diabetes datasets
dataset = datasets.load_diabetes()

# fit a linear regression model to the data
model = LinearRegression()
model.fit(dataset.data, dataset.target)
print(model)

# make predictions
expected = dataset.target
predicted = model.predict(dataset.data)

# >>>>>>>Print out the statistics<<<<<<<<<<<<<
model.summary()

# summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print model.intercept_, model.coef_, mse, 
print(model.score(dataset.data, dataset.target))

Answered By: Vinicius Barcelos

Answer 3

statsmodels package gives a quiet decent summary

from statsmodels.api import OLS
OLS(dataset.target,dataset.data).fit().summary()

Answered By: Akshay Dalal

Answer 4

I use:

import sklearn.metrics as metrics
def regression_results(y_true, y_pred):

    # Regression metrics
    explained_variance=metrics.explained_variance_score(y_true, y_pred)
    mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) 
    mse=metrics.mean_squared_error(y_true, y_pred) 
    mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)
    median_absolute_error=metrics.median_absolute_error(y_true, y_pred)
    r2=metrics.r2_score(y_true, y_pred)

    print('explained_variance: ', round(explained_variance,4))    
    print('mean_squared_log_error: ', round(mean_squared_log_error,4))
    print('r2: ', round(r2,4))
    print('MAE: ', round(mean_absolute_error,4))
    print('MSE: ', round(mse,4))
    print('RMSE: ', round(np.sqrt(mse),4))

Answered By: Naomi Fridman

Answer 5

You can do using statsmodels

import statsmodels.api as sm
X = sm.add_constant(X.ravel())
results = sm.OLS(y,x).fit()
results.summary()

results.summary() will organize the results into three tabels

Answered By: Sahil Kamboj

Answer 6

You can use the following option to have a summary table:

import statsmodels.api as sm

#log_clf = LogisticRegression()

log_clf =sm.Logit(y_train,X_train)

classifier = log_clf.fit()

y_pred = classifier.predict(X_test)

print(classifier.summary2())

Answered By: jans castellon

How to get a regression summary in scikit-learn like R does?

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