What approaches can be used to improve model quality after achieving a baseline?

After achieving the basic model result, you can improve quality with the following approaches:

• Collecting and adding new data — expanding and diversifying the training set often enhances the model's generalization ability.
• Hyperparameter tuning — selecting optimal model parameters using Grid Search, Random Search, or Bayesian Optimization.
• Using more complex or suitable model architectures — for example, transitioning from simple models to ensembles or deep neural networks.
• Data processing and cleaning — removing noise, handling missing values, normalizing, and scaling features.
• Feature engineering — creating new features that better reflect patterns in the data.
• Regularization — preventing overfitting with L1, L2 regularization, or Dropout.
• Model ensembling — combining multiple models to improve stability and accuracy.
• Cross-validation — for more reliable quality assessment and model selection.

These methods help increase the accuracy, robustness, and generalization ability of the model.