The article discusses the shift in psychology from an emphasis on explaining the causes of behavior to a greater focus on prediction. Traditionally, psychology has prioritized explanatory models, aiming to understand the causal mechanisms behind behavior through tightly controlled experiments. However, this approach often leads to models that are theoretically appealing but lack predictive accuracy. The authors argue that principles and techniques from machine learning can help psychology become a more predictive science. They highlight the importance of prediction in psychological research, noting that while explanation and prediction are philosophically intertwined, they often conflict statistically and practically. Machine learning offers tools to improve predictive accuracy, such as cross-validation and regularization, which help prevent overfitting. The article also addresses the issue of overfitting, where models perform well on training data but poorly on new data. It emphasizes the need for a balance between bias and variance in models, and the importance of using large datasets to reduce overfitting. The authors suggest that psychology should adopt a more predictive approach, using large datasets and cross-validation to improve the reliability and generalizability of findings. They argue that this shift can lead to a better understanding of behavior in the long term. The article also discusses the challenges of small-sample studies and the importance of large-scale, multi-lab studies to ensure replicability and reliability. Overall, the authors advocate for a more predictive approach in psychology, emphasizing the need for rigorous methods to improve the accuracy and generalizability of psychological research.The article discusses the shift in psychology from an emphasis on explaining the causes of behavior to a greater focus on prediction. Traditionally, psychology has prioritized explanatory models, aiming to understand the causal mechanisms behind behavior through tightly controlled experiments. However, this approach often leads to models that are theoretically appealing but lack predictive accuracy. The authors argue that principles and techniques from machine learning can help psychology become a more predictive science. They highlight the importance of prediction in psychological research, noting that while explanation and prediction are philosophically intertwined, they often conflict statistically and practically. Machine learning offers tools to improve predictive accuracy, such as cross-validation and regularization, which help prevent overfitting. The article also addresses the issue of overfitting, where models perform well on training data but poorly on new data. It emphasizes the need for a balance between bias and variance in models, and the importance of using large datasets to reduce overfitting. The authors suggest that psychology should adopt a more predictive approach, using large datasets and cross-validation to improve the reliability and generalizability of findings. They argue that this shift can lead to a better understanding of behavior in the long term. The article also discusses the challenges of small-sample studies and the importance of large-scale, multi-lab studies to ensure replicability and reliability. Overall, the authors advocate for a more predictive approach in psychology, emphasizing the need for rigorous methods to improve the accuracy and generalizability of psychological research.