- Add more data. Having more data is always a good idea.
- Treat missing and Outlier values.
- Feature Engineering.
- Feature Selection.
- Multiple algorithms.
- Algorithm Tuning.
- Ensemble methods.
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Then, how can we improve the performance of random forest?
There are three general approaches for improving an existing machine learning model:
- Use more (high-quality) data and feature engineering.
- Tune the hyperparameters of the algorithm.
- Try different algorithms.
Additionally, how do you improve random forest prediction in R? To improve our technique, we can train a group of Decision Tree classifiers, each on a different random subset of the train set. To make a prediction, we just obtain the predictions of all individuals trees, then predict the class that gets the most votes. This technique is called Random Forest.
In respect to this, how can decision tree accuracy be improved?
Try to use another data sets, or cross-validation to see the more accurate result. By the way, 90%, if not overfitted, is great result, may be you even don't need to improve it. You could look into pruning the leaves to improve the generalization of the decision tree.
What is a good accuracy score?
If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error.
How do you stop Overfitting in random forest?
- n_estimators: The more trees, the less likely the algorithm is to overfit.
- max_features: You should try reducing this number.
- max_depth: This parameter will reduce the complexity of the learned models, lowering over fitting risk.
- min_samples_leaf: Try setting these values greater than one.
What is the final objective of decision tree?
