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ML emerges from AI and computer science, focusing on data-driven algorithms. SL is rooted in statistics, emphasizing mathematical model construction.
ML enables systems to learn and improve from experience using algorithms to make decisions.
SL uses mathematical models for data analysis, stressing on pattern understanding and uncertainty.
ML prioritizes prediction and efficiency. SL values interpretability and inference.
ML talks 'training' and 'test' datasets, while SL uses 'estimation' and 'validation.'
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ML models can be complex, like neural networks. SL prefers simpler, interpretable models.
ML focuses on predictive accuracy. SL examines statistical significance and confidence.
Both ML and SL employ regularization, like Lasso and Ridge regression, to enhance model reliability.
ML excels in handling large datasets with computational power. SL seeks efficiency through simpler methods.
ML aims for optimal predictions. SL seeks to understand data's structure and uncertainty.
For a deeper understanding, explore the full article on Machine Learning vs. Statistical Learning.