Imitating human gameplay is one of the important factors in entertaining or teaching human players. In this paper, we target the game of Go at amateur levels and investigate KataGo’s HumanSL model, which was trained on a large number of human game records using supervised learning (SL). Specifically, we (1) evaluate the human-likeness of HumanSL policies (i.e., probability distribution of moves) at various skill levels from several aspects, (2) propose a method called Policy Adjustment based on Previous Move (PAPM) to enhance the human-likeness of HumanSL policies, and (3) demonstrate that selecting moves using a softmax function with appropriate parameter settings can better fit the specified skill levels. Additionally, we conduct a preliminary experimental study to compare the probability distributions of HumanSL and PAPM with softmax (PAPM+SX). The results indicate that the participants found PAPM+SX to be more human-like than HumanSL.