Get LogisticRegression "coef_" Attribute

Understanding the weights of the features in a logistic regression model is crucial for interpreting the model. By retrieving and interpreting the coef_ attribute of a fitted LogisticRegression model, you can gain insights into the influence of each feature on the predictions.

LogisticRegression is a linear model used for binary classification tasks, which fits a logistic function to the data. The coef_ attribute of a fitted LogisticRegression model stores the coefficients (weights) for each feature, indicating their influence on the prediction. Accessing the coef_ attribute is useful for feature interpretation and understanding the impact of each feature on the model’s predictions. This can guide feature selection and model refinement.

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Generate a synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=42, shuffle=False)

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize a LogisticRegression model
lr = LogisticRegression(random_state=42)

# Fit the model on the training data
lr.fit(X_train, y_train)

# Access the coef_ attribute and print the coefficients for each feature
coefficients = lr.coef_
print(f"Coefficients of the logistic regression model: {coefficients}")

Running the example gives an output like:

Coefficients of the logistic regression model: [[-0.26262277  2.1285876   0.16353088 -0.08982462]]

The key steps in this example are:

1. Generate a synthetic binary classification dataset using make_classification and split it into train and test sets.
2. Initialize a LogisticRegression model and fit it on the training data.
3. Access the coef_ attribute to retrieve the coefficients of the fitted model.
4. Print the coefficients to interpret the influence of each feature on the model’s predictions.