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Clean and scale your data for reliable ML models. Handle missing values and normalize datasets to boost algorithm performance.
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Use k-fold cross-validation to ensure your model generalizes well. It splits data into subsets to train and validate effectively.
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Boost model performance by creating and selecting the right features. Use RFE or RFECV to reduce overfitting and enhance interpretability.
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Optimize your model with Grid Search or Random Search. Proper tuning ensures your model performs at its best.
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Pick metrics that suit your project. For imbalanced data, focus on precision, recall, or F1 score instead of accuracy.