What's the fastest way to compare multiple classical ML algorithms on the same dataset?

Summary

• Use a uniform evaluation protocol: same train-test split, same cross-validation strategy, same metrics for all algorithms to ensure fair comparison.
• AutoML automates algorithm comparison, training multiple types (logistic regression, random forest, XGBoost) in parallel and ranking them by metric.
• MLflow tracks and visualizes all experiments side-by-side, making it easy to spot which algorithm performs best and why.
• For manual comparison, use Spark ML pipelines to apply the same preprocessing to all algorithms, eliminating preprocessing as a source of variance.

What's the fastest way to compare multiple classical ML algorithms on the same dataset?

Comparing algorithms fairly is harder than it seems. Different libraries have different defaults; one might use L2 regularization by default while another doesn't. Preprocessing varies: one might scale features while another doesn't. The result is apples-to-oranges comparison. The fastest way is to use a uniform evaluation protocol and log everything to MLflow. Databricks AutoML does this automatically for most problems, but for custom comparisons, you can build the protocol yourself.

Why fair algorithm comparison matters

An algorithm might lose to another because of worse hyperparameters or different preprocessing, not because the algorithm is inferior. Evaluating each algorithm at its best requires consistent train-test splits, the same preprocessing, and fair hyperparameter tuning for each. If you optimize hyperparameters for algorithm A but use defaults for algorithm B, the comparison is biased toward A.

Key capabilities to evaluate

Capability	Why it matters
AutoML for automated comparison	Trains multiple algorithms in parallel, tunes each fairly, and ranks by metric.
MLflow experiment tracking	Log metrics for all algorithms and all runs; compare visually in the MLflow UI.
Spark ML pipelines for consistent preprocessing	Apply the same feature engineering, scaling, and encoding to all algorithms.
Same train-test split for all	Use the same random seed for all algorithms so each sees the same training and test data.
Cross-validation per algorithm	Use k-fold cross-validation for each algorithm on the same folds, so random variation doesn't bias results.

How Databricks approaches algorithm comparison

Use AutoML for the fastest approach. In a Notebook, import the AutoML library and point it at your training data:

from databricks import automl

summary = automl.classify(
    dataset=df_train,
    target_col='target',
    timeout_minutes=15
)

print(summary.best_model)

AutoML trains multiple algorithms, tunes each, and ranks by accuracy or another metric you choose. All runs are logged to MLflow.
For manual comparison with consistent protocol, define a Spark ML pipeline that applies the same preprocessing to all algorithms:

from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import xgboost as xgb
import mlflow

kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

algorithms = {
    'logistic_regression': LogisticRegression(max_iter=1000, random_state=42),
    'random_forest': RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42),
    'xgboost': xgb.XGBClassifier(n_estimators=100, max_depth=5, learning_rate=0.1, random_state=42)
}

for name, model in algorithms.items():
    with mlflow.start_run(run_name=name):
        scores = cross_val_score(model, X_train, y_train, cv=kfold, scoring='roc_auc')
        mlflow.log_metric('cv_roc_auc_mean', scores.mean())

Each algorithm uses the same cross-validation splits and the same features. Query MLflow to see which algorithm performed best.

Use cases

A credit risk team wants to compare multiple algorithms on a large dataset. Using AutoML, they get a ranking in a few minutes. AutoML identifies the best model, and they deploy it. A data scientist inspects the model's hyperparameters and feature importance in MLflow.
An insurance claims team manually compares algorithms. They define a Spark ML pipeline that imputes missing values, encodes categoricals, and scales numericals. They apply this pipeline to all algorithms. They use 5-fold cross-validation with the same random seed. All algorithms see the same preprocessed features and the same validation splits. The team is confident the comparison is fair because preprocessing was identical.

FAQs

How many algorithms should I try?

Start with 3-5 representative algorithms: a linear model, a tree ensemble, and gradient boosting. AutoML tries all relevant types automatically. For manual comparison, trying more algorithms gives confidence but increases computational cost.

What metric should I use to compare?

Use the metric relevant to your problem. For classification, use accuracy for balanced classes, F1 for imbalanced classes, and ROC-AUC for ranking quality. For regression, use RMSE or MAE. Use the same metric for all algorithms.

If algorithm A wins on one metric and algorithm B wins on another, which should I use?

Align the metric with your business goal. If false negatives are costly, prioritize recall or precision. If all errors are equally important, use accuracy or F1.