AutoML for classification and regression tasks differs primarily in the problem type they address, the algorithms and evaluation metrics they prioritize, and the preprocessing steps they automate. Classification involves predicting discrete labels (like “spam” or “not spam”), while regression predicts continuous numerical values (like house prices). AutoML frameworks detect the task type based on the target variable’s data format and adjust their workflows accordingly. For example, if the target is categorical (e.g., strings or integers representing classes), AutoML defaults to classification; if it’s numerical, regression is assumed. This distinction impacts how data is preprocessed—classification may require label encoding or handling class imbalance, while regression might focus on scaling features or detecting outliers.
The choice of algorithms and evaluation metrics also varies. For classification, AutoML often prioritizes models like decision trees, logistic regression, or support vector machines, which are designed to separate classes. Metrics like accuracy, F1-score, or AUC-ROC are used to evaluate performance. In contrast, regression tasks typically use algorithms like linear regression, gradient-boosted trees, or neural networks optimized for minimizing prediction errors. Metrics such as mean squared error (MSE), R-squared, or mean absolute error (MAE) are standard. AutoML frameworks will automatically select metrics aligned with the task—for instance, avoiding accuracy for regression, as it’s meaningless when predicting continuous values. A practical example: predicting customer churn (classification) would optimize for precision/recall, while forecasting sales (regression) would minimize RMSE.
Finally, AutoML handles feature engineering and hyperparameter tuning differently. For classification, frameworks might automatically encode categorical variables, balance classes via oversampling, or handle multilabel outputs. Regression tasks might focus on detecting nonlinear relationships (e.g., generating polynomial features) or normalizing numerical inputs. Hyperparameter tuning also diverges: classification models might prioritize parameters like max_depth in decision trees to avoid overfitting, while regression models could tune regularization terms (e.g., L1/L2 in linear models) to control coefficient magnitudes. Developers should verify that the AutoML tool correctly infers the task type, as misclassification (e.g., treating a numerical target as categorical) can lead to nonsensical models. Tools like Auto-Sklearn or H2O.ai explicitly separate these workflows, ensuring task-specific optimizations.