Classification Models Compared

When the outcome is a category — churn or stay, fraud or legit, approve or deny — you reach for a classifier. The question is which one, and how to know it actually works.

The Main Models

Logistic Regression

Linear, fast, and interpretable — you get coefficients you can explain. A strong baseline; needs scaling and roughly linear boundaries.

Decision Tree

Simple if-then splits, easy to read. Prone to overfitting on its own, but the foundation for far stronger ensembles.

Random Forest

Many trees voting together. Robust, low-maintenance, handles non-linearity — a reliable default.

Gradient Boosting / XGBoost

Trees built sequentially to fix each other's errors. Usually the top performer on tabular data, at the cost of tuning.

SVM & KNN

SVM finds a maximum-margin boundary (scale-sensitive); KNN classifies by nearest neighbors (simple, but slow at scale). Both need scaled features.

Which to Choose

Metrics That Matter

• Precision: of the positives you predicted, how many were right
• Recall: of the actual positives, how many you caught
• F1: the balance of precision and recall
• ROC-AUC: ranking quality across every threshold

The confusion matrix above is the source of all of these — read it before trusting any single score.

Common Mistakes

• Judging an imbalanced problem by accuracy alone
• No train/test split — reporting scores the model has already seen
• Leaking information by scaling or imputing before the split
• Tuning XGBoost to death when a random forest was already enough

Comparing Models in the SKARI Model Lab

The Model Lab is built for exactly this: train several classifiers on the same data and compare them head to head — no code, no glue.

• Fit logistic regression, random forest, XGBoost, SVM and more on one dataset
• Compare accuracy, precision, recall, F1 and ROC-AUC side by side
• Inspect the confusion matrix and feature importance for each model

Frequently Asked Questions

Which classifier should I start with?

Start with logistic regression as a baseline and a random forest as a strong default. Move to XGBoost when you need the last few points of accuracy.

Do these models need scaling?

Logistic regression, SVM and KNN do; tree-based models (random forest, XGBoost) are scale-invariant.

Accuracy or F1?

Use F1 (or precision/recall directly) whenever the classes are imbalanced or the two error types have different costs.

Conclusion

There's no single best classifier — only the best one for your data, your balance, and your tolerance for a black box. Compare fairly, and judge by the right metric.