The article "Choosing prediction over explanation in psychology: Lessons from machine learning" by Tal Yarkoni and Jacob Westfall argues that psychology has traditionally focused heavily on explaining the causal mechanisms behind behavior, often at the expense of predictive accuracy. They propose that principles and techniques from machine learning can help psychology become a more predictive science. The authors review fundamental concepts and tools in machine learning, such as overfitting, cross-validation, and regularization, and illustrate how these can be applied to psychological research. They emphasize the importance of balancing flexibility and robustness in models, suggesting that a focus on prediction can ultimately enhance our understanding of behavior. The article also discusses the challenges of overfitting, the impact of p-hacking, and the benefits of large datasets in improving predictive models.The article "Choosing prediction over explanation in psychology: Lessons from machine learning" by Tal Yarkoni and Jacob Westfall argues that psychology has traditionally focused heavily on explaining the causal mechanisms behind behavior, often at the expense of predictive accuracy. They propose that principles and techniques from machine learning can help psychology become a more predictive science. The authors review fundamental concepts and tools in machine learning, such as overfitting, cross-validation, and regularization, and illustrate how these can be applied to psychological research. They emphasize the importance of balancing flexibility and robustness in models, suggesting that a focus on prediction can ultimately enhance our understanding of behavior. The article also discusses the challenges of overfitting, the impact of p-hacking, and the benefits of large datasets in improving predictive models.