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Speculative multithreading (SpMT) is a thread-level automatic parallelization technique to accelerate sequential programs. Machine learning has been successfully brought into SpMT to improve its performance. An appropriate sample set, which plays the role of knowledge provider, is important for machine learning-based (ML-based) thread partition. Conventionally, heuristic rules-based (HR-based) sample generation approach cannot generate adaptive samples. A hybrid sample generation approach can break this bottleneck. With this method, we firstly automatically generate samples, which are MIPS codes consisting of spawning points (SPs) and control quasi-independent points (CQIPs) by heuristic rules; secondly manually adjust the positions of SPs and CQIPs and rebuild pre-computation slice to obtain better performance for every sample; and then build model to ensure that the probability of adjusting to the optimal partition positions is increasing. During the implementation of this approach, three measures: bias weighting, preservation of optimal solutions, summary of greedy rules, are taken. In this way, we enhance the adjustment frequency for subroutines with high called time and preserve the optimal partition positions, so to achieve a stable speedup improvement. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, SPEC2000 and Olden benchmarks are used as input. Experiments show that our approach can obtain better sample sets, which deliver a better performance improvement of about 86.9% on a 16 core than the samples generated by HR-based approach. Experiment results also prove that this approach is effective to generate sample sets for ML-based thread partition. [ABSTRACT FROM AUTHOR]