Imbalanced Data
Try evenness sampling or another method for the data. Hedge trees, that make a decision, may be closer on the side of the class that provides more instances. Here you can decrease the class size you wanted to increase, or increase some other class to find a competent balance.
- Example: In a fraud detection system, imbalanced data with very few fraud cases compared to legitimate transactions can bias the model towards predicting all transactions as legitimate.
- Prevention: Use techniques like oversampling, undersampling, or synthetic data generation to balance the classes and improve model performance.
Learn More about How to Handle Imbalanced Classes in Machine Learning
How to Avoid Common Mistakes in Decision Trees
Decision trees are powerful tools in machine learning, but they can easily fall prey to common mistakes that can undermine their effectiveness. In this article, we will discuss 10 common mistakes in Decision Tree Modeling and provide practical tips for avoiding them.
Technique to Avoid Common Mistakes in Decision Trees
- Overfitting
- Lack of Data
- Picking Features
- Imbalanced Data
- Not Considering Domain Knowledge
- Inconsistent Data
- Limited Tree Depth
- Skipping Model Validation
- Overlooking Extra Costs
- Shortcomings in some Models in Efforts to Renew
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