Getting weights of features using scikit-learn Logistic Regression

Getting Weights of Features Using scikit-learn Logistic Regression

Logistic Regression is a powerful classification algorithm widely used in machine learning. One of its key advantages is the ability to interpret the importance of different features in making predictions. This interpretation is facilitated by the weights (or coefficients) associated with each feature in the model. These weights quantify the influence of each feature on the prediction, enabling us to understand which features are most crucial for the model’s decision-making process.

Understanding Weights

In Logistic Regression, the weights are learned during the model training process. They represent the strength and direction of the relationship between each feature and the target variable. A positive weight indicates a positive association, meaning an increase in the feature’s value leads to a higher probability of the positive class. Conversely, a negative weight indicates a negative association, suggesting a higher value of the feature decreases the probability of the positive class.

Example: Predicting Customer Churn

Let’s imagine we are building a model to predict customer churn (whether a customer will discontinue their subscription). We might have features like:

• Age
• Monthly bill amount
• Number of calls to customer support

After training our Logistic Regression model, we might observe the following weights:

From these weights, we can infer:

• Older customers (higher age) are less likely to churn (negative weight).
• Customers with higher monthly bills are more likely to churn (positive weight).
• Customers who make more calls to customer support are more likely to churn (positive weight, the strongest among the three).

Retrieving Weights with scikit-learn

The scikit-learn library provides a straightforward way to access the weights of a trained Logistic Regression model. The weights are stored within the coef_ attribute of the fitted model object.

Code Example

 from sklearn.linear_model import LogisticRegression from sklearn.datasets import load_iris # Load the Iris dataset iris = load_iris() X = iris.data y = iris.target # Create and fit the Logistic Regression model model = LogisticRegression() model.fit(X, y) # Access the weights weights = model.coef_ # Print the weights for each feature print(weights) 

The output will be a 2D array where each row represents the weights for a particular class and each column corresponds to a feature. For example, the output might look like this:

 [[ 0.45190182 -0.56083234 0.05744955 0.17595271] [-1.01822886 0.82403893 -0.13748285 -0.39420528]] 

Interpreting the Weights

The obtained weights can be interpreted to identify the most influential features. Features with larger absolute weight values exert a greater impact on the model’s predictions. The sign of the weight indicates the direction of the relationship.

Caution

It is essential to remember that feature weights should be interpreted within the context of the specific dataset and model. The importance of a feature can be relative to other features and may not always provide an absolute measure of its impact on the real-world phenomenon. Furthermore, interactions between features can influence the overall prediction, which may not be fully captured by individual weights alone.

Conclusion

Understanding feature weights in Logistic Regression empowers us to gain valuable insights into the model’s decision-making process. By leveraging the coef_ attribute in scikit-learn, we can identify the most impactful features and comprehend their relationships with the target variable. This knowledge helps us to interpret the model’s predictions, improve its performance by addressing potential issues, and gain a deeper understanding of the underlying data.