How do I improve the performance of my Random Forest model on Databricks?

[Suheb]

How can I make these people smarter or faster so the final answer is better?

[Louis_Frolio · Accepted solution]

Greetings @Suheb , your question is very broad in nature but I can offer you some general high level guidelines. 

Improving a random forest is rarely about clever tricks. Most of the gains come from better data preparation, the right evaluation setup, and disciplined hyperparameter tuning. The mechanics are largely the same for classification and regression, with a few important differences in how you tune and evaluate.

Start with the data and the target

Before touching hyperparameters, make sure the fundamentals are solid.

 Clean the obvious issues first: missing values, data leakage, label errors, and duplicate rows. Random forests are robust, but they are not immune to bad data.

Then look closely at the target itself.

For classification, check for class imbalance and confirm labels are meaningful and consistent.

For regression, inspect the distribution for extreme outliers or heavy tails. In some cases, a simple target transformation (for example, logging the target) can dramatically stabilize learning.

Choose the right evaluation setup

Decide upfront how you will measure success.

Use a clean train/validation/test split or cross-validation, and choose metrics that actually reflect the problem you’re solving.

For classification, accuracy alone is often misleading—ROC-AUC, PR-AUC, F1, or balanced accuracy are usually more informative.

For regression, RMSE, MAE, and R² together give a better picture than any single metric.

If classes are imbalanced, optimize for metrics that penalize false confidence rather than raw accuracy.

Improve features before tuning models

Random forests do not require heavy preprocessing, but feature quality still matters.

Encode categorical variables correctly. One-hot encoding works well for low-cardinality features; for very high-cardinality categories, target encoding or grouping strategies may help.

Scaling is usually unnecessary for trees, but engineered features—ratios, logs, domain-specific aggregates—can still provide meaningful signal.

If you have many weak or irrelevant predictors, prune aggressively. Removing noise often helps more than adding complexity. Feature importance or RFE can be useful when dimensionality is high.

Tune the core random forest hyperparameters

This is where most people focus—but it should come after the steps above.

Start with the number of trees. Increase n_estimators until validation performance plateaus. More trees improve stability and reduce variance, but they increase training cost.

Control overfitting through tree structure. Parameters like max_depth, min_samples_split, and min_samples_leaf matter more than people expect. Shallower trees and larger leaf sizes usually generalize better.

Classification vs regression nuances

For classification:

Tune max_features carefully. Smaller values decorrelate trees, which often improves performance and calibration.

If classes are imbalanced, use class_weight or balanced subsampling instead of naïve oversampling.

For regression:

max_features still matters, but slightly larger values can help capture interactions in continuous targets.

Track both RMSE and MAE. Divergence between them is often a sign that outliers are driving error, which may require adjusting depth or leaf size.

Use systematic search, not guesswork

Avoid manual tuning by intuition alone.

Use GridSearchCV, RandomizedSearchCV, or Bayesian optimization over a reasonable range of key parameters: n_estimators, max_depth, max_features, min_samples_split, and min_samples_leaf.

Always evaluate candidates with cross-validation. Once you’ve selected the best configuration, retrain on the full training set and report final performance on a held-out test set. That last step matters more than people admit.

Diagnose, then decide what’s next

After training, inspect the model.

Look at feature importance and partial dependence or ICE plots to verify the model is relying on sensible signals. These tools often surface data issues or missing interactions faster than raw metrics.

If performance plateaus, that’s usually a sign—not a failure. At that point, the biggest gains typically come from better data, richer feature engineering, or switching to a different ensemble method (for example, gradient boosting) that’s better suited to the problem.

Takeaway

Random forests reward discipline more than cleverness. Clean data, honest evaluation, thoughtful features, and systematic tuning will almost always beat ad-hoc tweaks. When you hit a ceiling, listen to what the model is telling you—it’s often pointing back to the data.

Hope this guidance helps you understand more and provide you with an approach.

Cheers, Louis.

[iyashk-DB · Accepted solution]

Hi @Suheb , 

For large datasets or distributed training, use Apache Spark MLlib RandomForest on Databricks; trees are trained in parallel and scale with cluster size.
Ref Doc -  https://www.databricks.com/blog/2015/01/21/random-forests-and-boosting-in-mllib.html

Index categorical features (StringIndexer) and avoid one-hot encoding; ensure maxBins is at least the highest categorical cardinality so splits are meaningful.
Cache the prepared training data before fitting—Spark tree algorithms benefit from caching due to iterative passes over data.
Use Optuna with MLflow 3 on Databricks ML Runtime 17.0+ for parallel, distributed searches (MlflowSparkStudy + MlflowStorage), and autolog runs to MLflow.
Tune key RF params: numTrees, maxDepth, featureSubsetStrategy (“auto”), minInstancesPerNode, and maxBins. Increasing trees usually improves test error but increases training time; keep trees reasonably deep to avoid overfitting and long runtimes.
Use CrossValidator or TrainValidationSplit with MLlib, Put your data prep stages outside the cross-validator (wrap the CV inside the Pipeline), so prep isn’t re-run for every hyperparam trial; this saves time at scale.

If Random Forest plateaus, try XGBoost via xgboost.spark for distributed gradient boosting within Spark ML Pipelines; set num_workers to sc.defaultParallelism for full-cluster training.

[sasadarsh]

hi i have the same problem

[jameswood32]

Improving the performance of a Random Forest model on Databricks is usually about data quality, feature engineering, and hyperparameter tuning. Some tips:

1. Feature Engineering:

   • Create meaningful features and remove irrelevant ones.

   • Encode categorical variables properly (one-hot, target encoding).

   • Normalize or scale features if needed (helps some metrics).

2. Hyperparameter Tuning:

   • Number of trees (numTrees): More trees can improve accuracy but increase training time.

   • Maximum depth (maxDepth): Controls overfitting; tune carefully.

   • Minimum samples per leaf / split (minInstancesPerNode): Helps generalization.

   • Use Databricks Hyperopt or MLlib tuning for automated search.

3. Sampling & Data Handling:

   • Use stratified sampling if classes are imbalanced.

   • Remove duplicates or irrelevant rows.

   • Check for missing values and handle them appropriately.

4. Parallelism & Resources:

   • Databricks allows distributed training; leverage Spark’s parallelism to train larger forests faster.

   • Cache frequently used datasets in memory for faster access.

5. Feature Importance & Selection:

   • Use feature importance from the trained model to drop low-impact features.

   • Fewer, high-quality features often lead to better performance.

6. Ensemble Tuning:

   • Sometimes combining Random Forest with Gradient Boosting (or stacking) can boost performance.

Pro Tip: Always validate improvements using a separate test set or cross-validation to avoid overfitting.