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