Avoid these common mistakes when creating linear models in Python

Linear regression is a fundamental technique in machine learning used for predicting a continuous target variable based on one or more predictor variables. However, people often make common mistakes when working with linear regression, and in this thread, we will discuss some of these mistakes and provide correct code snippets.

1. Not splitting data into training and testing sets:

Forgetting or failing to split the data can lead to overfitting and leave you with no unseen data that you can use to test the model. To resolve this, split the complete data into training and testing sets using the train_test_split function as shown below:

2. Not scaling the features:

Another common mistake individuals make is not scaling the data before training the model. Linear regression models are sensitive to the scale of the features, and not scaling the data features can lead to incorrect coefficient values and inaccurate results. To tackle this mistake, the code below scales the features before training using StandardScaler:

3. Neglecting regularization:

Regularization techniques like Ridge and Lasso regression are essential for preventing overfitting in linear regression models. Using a simple linear model and neglecting regularization can result in models that are too complex and do not generalize well to unseen data. To resolve this mistake, apply regularization when necessary, and the code below shows how you can use Ridge regularization.

4. Ignoring multicollinearity of features:

Multicollinearity refers to a situation in which two or more independent variables in a regression model are highly correlated with one another. When multicollinearity is ignored or not addressed in a linear regression analysis, it can lead to several issues and misinterpretations. The code below calculates the VIF score to detect the multicollinearity of each feature:

  • Handling multicollinearity may involve dropping one of the correlated features or using techniques like Principal Component Analysis (PCA) to create uncorrelated variables.
  • Addressing multicollinearity ensures that the linear regression model’s coefficients are more stable and interpretable, leading to better model performance.