91.20
91.23
91.37
91.15
91.05
91.29
91.26
90.96
91.40
91.60

54.79
49.25
42.67
51.25
52.03
31.83
31.75
41.00
40.98
25.58

90.76
91.13
91.22
90.93
90.78
91.09
91.17
90.69
91.26
91.38

73.16
54.33
43.50
64.67
72.97
43.33
47.91
90.42
52.43
38.67

Table 4: Results on HyperFD.

5.3 Compared baselines

We show the performance of MLCopilot in Table 2 (HPO-B), Table 3 (PD1) and Table 4 (HyperFD),
compared with the following traditional baselines:

• Random method randomly generates a solution.
• ASKL (Auto-sklearn 1.0) [12] ﬁnds the most similar tasks based on manually selected meta-

features of tasks and directly uses the best solutions on them.

• Constant [3, 17] (a.k.a. Average) uses a constant set of solutions for any new task. The produced

set of solutions is the one with the best average performance on the historical tasks.
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks