Statistical Learning vs Machine Learning: Key Differences Explained

In the world of data science, two dominant paradigms for analyzing data and building predictive models are Statistical Learning and Machine Learning. While they often overlap, their goals, assumptions, and methodologies are distinct.

In this guide, we break down what each entails, how they differ, and when to use them based on your use case.

What Is Statistical Learning?

Statistical learning is a subfield of statistics that focuses on understanding the relationship between input variables (X) and an outcome (Y). It’s often grounded in theory, interpretability, and assumptions.

Common statistical learning methods include:

• Linear Regression
• Logistic Regression
• Generalized Additive Models (GAMs)
• Ridge/Lasso Regression

These models rely on well-defined assumptions (e.g., normality, independence, homoscedasticity) and aim for interpretability over complexity.

Example: Linear Regression in R

R

model <- lm(y ~ x1 + x2, data = dataset)
summary(model)

What Is Machine Learning?

Machine learning is a broader term that emphasizes predictive accuracy over interpretability. It includes a wider range of algorithmic models that may or may not be based on statistical theory.

Popular ML algorithms:

• Decision Trees
• Random Forests
• Support Vector Machines
• Neural Networks
• Gradient Boosting Machines (e.g., XGBoost, LightGBM)

ML models often make fewer assumptions about the underlying data and excel at learning complex, nonlinear relationships.

Example: Random Forest in Python

python

from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

Core Differences Between Statistical Learning Vs Machine Learning

lm(price ~ sqft + bedrooms + location, data = house_data)

import xgboost as xgb
model = xgb.XGBRegressor()
model.fit(X_train, y_train)