How to implement polynomial logistic regression in scikit-learn?

Question

I'm trying to create a non-linear logistic regression, i.e. polynomial logistic regression using scikit-learn. But I couldn't find how I can define a degree of polynomial. Did anybody try it? Thanks a lot!

Clerc

Answer 1

For this you will need to proceed in two steps. Let us assume you are using the iris dataset (so you have a reproducible example):

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline

data = load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y)

Step 1

First you need to convert your data to polynomial features. Originally, our data has 4 columns:

X_train.shape
>>> (112,4)

You can create the polynomial features with scikit learn (here it is for degree 2):

poly = PolynomialFeatures(degree = 2, interaction_only=False, include_bias=False)
X_poly = poly.fit_transform(X_train)
X_poly.shape
>>> (112,14)

We know have 14 features (the original 4, their square, and the 6 crossed combinations)

Step 2

On this you can now build your logistic regression calling X_poly

lr = LogisticRegression()
lr.fit(X_poly,y_train)

Note: if you then want to evaluate your model on the test data, you also need to follow these 2 steps and do:

lr.score(poly.transform(X_test), y_test)

Putting everything together in a Pipeline (optional)

You may want to use a Pipeline instead that processes these two steps in one object to avoid building intermediary objects:

pipe = Pipeline([('polynomial_features',poly), ('logistic_regression',lr)])
pipe.fit(X_train, y_train)
pipe.score(X_test, y_test)

Fenwick

Thanks a lot for the detailed explanation! – Clerc