Automatic unit test generation that explores the input space and produces effective test cases for given programs have been studied for decades. Many unit test generation tools that can help generate unit test cases with high structural coverage over a program have been examined. However, the fact that existing test generation tools are mainly evaluated on general software programs calls into question about its practical effectiveness and usefulness for machine learning libraries, which are statistically-orientated and have fundamentally different nature and construction from general software projects. In this paper, we set out to investigate the effectiveness of existing unit test generation techniques on machine learning libraries. To investigate this issue, we performed an empirical study on five widely-used machine learning libraries with two popular unit test case generation tools, i.e., EVOSUITE and Randoop. We find that (1) most of the machine learning libraries do not maintain a high-quality unit test suite regarding commonly applied quality metrics such as code coverage (on average is 34.1%) and mutation score (on average is 21.3%), (2) unit test case generation tools, i.e., EVOSUITE and Randoop lead to clear improvements in code coverage and mutation score, however, the improvement is limited, and (3) there exist common patterns of the uncovered code across the five machine learning libraries that can be used to improve unit test case generation tasks.