The　ball　screw　is　an　essential　component　in　feed　drive　systems　whose　accuracy　is　seriously　affected　by　machine　tool　internal　and　external　heat　sources.　In　this　paper,　a　thermal　error　compensation　method　for　ball　screws　is　proposed　based　on　the　extreme　gradient　boosting　(XGBoost)　algorithm　and　thermal　expansion　principle.　An　XGBoost　predictive　model　is　established　using　the　time　series　temperature　data　collected　from　thermal　characteristics　experiments.　Furthermore,　the　predictive　performance　between　the　XGBoost　algorithm　and　BP　neural　network　is　compared　to　validate　the　effectiveness　and　robustness　of　the　proposed　model.　The　results　show　that　the　XGBoost　model　has　better　predictive　performance.　Based　on　this,　the　temperature　of　key　points　on　the　ball　screw　can　be　obtained,　and　thermal　error　(which　is　useful　for　pulse　compensation)　is　predicted.　Simultaneously,　thermal　error　compensation　experiments　are　carried　out　on　the　ball　screw　bench　with　average　results　of　more　than　45%.　The　presented　thermal　error　compensation　method　proved　effective　and　can　provide　a　foundation　for　precision　machining.