Creating Interaction and Polynomial Features
PolynomialFeatures, manual interaction terms, ratio features, binning continuous variables.
Creating Interaction and Polynomial Features is a free Learn AI with Python lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Engineer New Features
Linear models only capture straight-line relationships. By creating interaction and polynomial features, you let simple models express curves and combined effects between variables.
What Are Interaction Features
An interaction multiplies two features, e.g. area = length * width. It captures effects that depend on both inputs together, which neither alone explains.
PolynomialFeatures Basics
PolynomialFeatures automatically generates powers and products of features up to a chosen degree. Degree 2 adds squares and pairwise products.
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
X = np.array([[2, 3]])
poly = PolynomialFeatures(degree=2)
print(poly.fit_transform(X))
# 1, x1, x2, x1^2, x1*x2, x2^2interaction_only Mode
Setting interaction_only=True keeps cross-products but drops pure powers like x1^2. This is useful when you only want combined effects, not curvature.
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)
X_int = poly.fit_transform(X)Watch the Feature Explosion
High degrees create many features and overfitting risk. With 10 inputs, degree 3 produces hundreds of columns. Keep degree low (2 or 3) and combine with feature selection or regularization.
Manual Ratio Features
Domain knowledge often beats brute force. Hand-crafted ratios like price per square meter or clicks per impression encode meaning that polynomial expansion would not find.
import pandas as pd
df["price_per_sqm"] = df["price"] / df["area"]
df["ctr"] = df["clicks"] / df["impressions"]Binning with pd.cut
Binning turns a continuous variable into categories. pd.cut splits a column into intervals, useful for capturing non-linear effects or grouping ages, incomes, etc.
import pandas as pd
df["age_group"] = pd.cut(
df["age"],
bins=[0, 18, 35, 60, 120],
labels=["minor", "young", "adult", "senior"],
)Equal-Frequency Binning
pd.qcut creates bins with roughly equal counts (quantiles), handy when the distribution is skewed and you want balanced groups.
import pandas as pd
df["income_quartile"] = pd.qcut(df["income"], q=4, labels=False)Handling Skew with Log Transform
Right-skewed features (income, prices, counts) hurt many models. A log transform compresses large values and makes the distribution more symmetric.
import numpy as np
# log1p handles zeros safely: log(1 + x)
df["log_income"] = np.log1p(df["income"])Square Root and Other Transforms
The square root transform also reduces skew but more gently than log, good for count data. Choose the transform that best symmetrizes the histogram.
import numpy as np
df["sqrt_count"] = np.sqrt(df["count"])
# inspect df["sqrt_count"].hist() to confirm reduced skewPutting It Together
Combine approaches: log-transform skewed columns, bin where thresholds matter, add domain ratios, and use PolynomialFeatures for interactions. Always validate that new features actually improve cross-validated scores.
Quick Check
Test your feature engineering knowledge.
Recap
Recap: Engineer features to help simple models. PolynomialFeatures with degree and interaction_only adds products and powers (watch the explosion). Build manual ratios from domain knowledge, bin with pd.cut/pd.qcut, and fix skew with log or sqrt transforms. Always validate gains.
Frequently asked questions
Is the “Creating Interaction and Polynomial Features” lesson free?
Yes — the full text of “Creating Interaction and Polynomial Features” is free to read here on the web, and the Learn AI with Python course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.
What will I learn in “Creating Interaction and Polynomial Features”?
PolynomialFeatures, manual interaction terms, ratio features, binning continuous variables. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Learn AI with Python?
No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Creating Interaction and Polynomial Features” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Learn AI with Python lesson?
Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Feature Selection Methods
- Creating Interaction and Polynomial Features
- Target Encoding and Advanced Categorical Handling
- Automated Feature Engineering with Featuretools