5 Tricks to Improve Model Accuracy in Machine Learning
Feature Engineering ✨:
TIP 01
Invest time in understanding your data and engineer informative features that capture relevant patterns and relationships.
Cross-Validation 🎯:
TIP 02
Utilize cross-validation techniques to assess model performance on unseen data and avoid overfitting.
Hyperparameter Tuning ⚙️:
TIP 03
Optimize model hyperparameters using techniques like grid search, random search, or Bayesian optimization.
Ensemble Learning 🤝:
TIP 04
Explore ensemble methods such as bagging, boosting, or stacking to improve model accuracy by combining multiple models.
Regularization Techniques ⚖️:
TIP 05
Apply regularization techniques like L1 and L2 regularization or dropout to mitigate overfitting and enhance generalization
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