Data Normalization In Machine Learning
Here’s Why We Use Data Normalization in Machine Learning?
Faster Convergence:
Helps models like neural networks learn faster by scaling features to the same range.
Prevents features with large values from overshadowing smaller ones.
Boosts Accuracy:
Improves predictions, especially for algorithms sensitive to scale like SVM and logistic regression.
Optimizes Distance-Based Models:
Enhances the performance of k-NN and clustering algorithms.
Handles Outliers:
Robust scaling reduces the impact of extreme values.
Streamlined Hyperparameter Tuning:
Simplifies selection of learning rates and regularization terms.
Faster Training:
Saves time by ensuring efficient computations.
Improves Generalization:
Works seamlessly with regularization techniques for better results.
Enhanced Visualizations:
Scaled data reveals clear patterns and trends.
Cross-Model Compatibility:
Prepares datasets for multiple algorithms without rework.
Data normalization ensures models not only work but thrive.
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