Focusing on the issue that the existing studies do not consider the imbalanced data distribution problem in defect datasets and employ improper performance measures to evaluate the performance of regression models for predicting the number of defects, the impact of different regression algorithms on models for predicting the number of defects were explored by using Fault-Percentile-Average (FPA) as the performance measure. Experiments were conducted on six datasets from PROMISE repository to analyze the impact on the models and the difference of ten regression algorithms for predicting the number of defects. The results show that the forecast results of models for predicting the number of defects built by different regression algorithms are various, and gradient boosting regression algorithm and Bayesian ridge regression algorithm can achieve better performance as a whole.