In　recent　years,　machine　learning　has　been　vastly　used　in　many　scientific　experiments　to　analyze　and　predict　the　data　[1].　Since　there　are　many　types　of　machine　learning　and　each　of　them　has　additional　further　branches,　it＇s　necessary　to　understand　which　one　can　predict　or　summarize　the　data　with　more　accuracy.　This　paper　mainly　focuses　on　traditional　machine　learning　and　active　learning.　To　compare　the　effects　of　these　two　types　of　machine　learning,　the　paper　uses　a　dataset　about　using　metal　oxide　semiconductor　(MOX)　to　measure　the　concentration　of　carbon　monoxide　(CO).　In　the　first　part,　the　paper　tests　the　accuracy　of　predicting　CO　concentration　with　3　types　of　traditional　machine　learning:　Decision　Tree,　Random　Forest　and　K-nearest　neighbors.　In　the　second　part,　the　paper　chooses　the　traditional　machine　learning　which　has　the　best　performance　in　the　first　part　and　compares　the　accuracy　of　it　with　the　active　learning　on　predicting　CO　concentration.　The　result　is　that　the　classification　accuracy　of　Random　Forest　is　0.625,　which　is　the　highest　among　them.　And　active　learning　is　generally　better　than　traditional　machine　learning　when　training　samples　are　small,　and　they　have　a　similar　accuracy　when　the　training　sample　is　enough.　©　2021　ACM.