Objective　To compare the prediction efficiency of traditional linear regression model and four machine learning models on the learning behavior of clinical medical postgraduates

and to explore the pros and cons and applicability of different prediction models. Methods　A total of 6

922 clinical medical postgraduates were surveyed

their comprehensive learning behavior scores were obtained through the learning behavior scale. In the training set

Lasso linear regression and artificial neural network

decision tree

Bootstrap random forest

and lifting tree were used to build prediction models respectively. The above models were used to predict the validation set data and compare the prediction efficiency. Results　The comprehensive learning behavior score of clinical medical postgraduates was (3.31±0.54) points

and the overall compliance rate was 74.02%. In the linear regression model

the influence of age

school level

degree type

learning interest

pressure and satisfaction on learning behavior were statistically significant. In the prediction of validation set

the sensitivity

specificity

and accuracy of the linear regression model were 0.484

0.914

and 0.801

respectively. The indexes of the four machine learning models were higher than those of the traditional linear regression model

and the Bootstrap random forest had the highest elevation. Conclusion　The linear regression model has a good prediction effect on learning behavior

and machine learning is superior to linear regression model in terms of accuracy of prediction. However

traditional linear regression models are superior to machine learning models in computational efficiency and interpretability.