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