Creating the pipeline Generating Polynomial Features
Incorporating a pipeline will streamline the workflow by combining steps like preprocessing and model training into a single process.
# Create a pipeline that generates polynomial features and trains a logistic regression model
pipeline = Pipeline([
('poly', PolynomialFeatures(degree=2)), # Generate polynomial features
('logistic', LogisticRegression()) # Train logistic regression model
])
# Train the pipeline
pipeline.fit(X_train, y_train)
Logistic Regression With Polynomial Features
Logistic regression with polynomial features is a technique used to model complex, non-linear relationships between input variables and the target variable. This approach involves transforming the original input features into higher-degree polynomial features, which can help capture intricate patterns in the data and improve the model’s predictive performance.
In this article we will understand the significance of Logistic Regression With Polynomial Features as well it’s implementation in scikit-learn.
Table of Content
- Understanding Polynomial Features with Logistic Regression
- Utilizing Logistic Regression with Polynomial Features
- Generate polynomial features
- Train the logistic regression model
- Creating the pipeline Generating Polynomial Features
- Advantages and Disadvantages of Logistic Regression With Polynomial Features