Train–test split

학습·평가 분할

Separate data used for fitting from data used for evaluation.

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html
<div class="stage"><div class="heading"><strong id="title"></strong><span id="status"></span></div><canvas id="plot"></canvas><div class="controls"><label id="control-label"></label><input id="parameter" type="range" min="0" max="100" value="55"><button id="restart" type="button">다시 보기</button></div></div>
css
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.heading{display:flex;justify-content:space-between;align-items:center;gap:8px;font-size:12px;white-space:nowrap}.heading strong{overflow:hidden;text-overflow:ellipsis}.heading span{color:var(--muted);font-size:11px}
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@media(max-width:500px){.stage{padding:7px 8px 6px}.controls{display:none}.heading{font-size:11px}.heading span{font-size:10px}}
js
const kind = "train-test-split", label = "학습·평가 분할";
const canvas = document.getElementById('plot'), ctx = canvas.getContext('2d');
const title = document.getElementById('title'), status = document.getElementById('status');
const slider = document.getElementById('parameter');
title.textContent = label;
const parameterNames={
 'feature-engineering':'원본 값','normalization-scaling':'선택 값','train-test-split':'학습 비율',
 'cross-validation':'평가 폴드','overfitting':'모델 복잡도','bias-variance':'복잡도',
 'regularization':'벌점 강도','linear-regression':'기울기','logistic-regression':'경계 기울기',
 'gradient-descent':'진행 단계','learning-rate':'학습률','loss-function':'잔차 보기',
 'decision-tree':'분할 기준','random-forest':'입력 값','k-nearest-neighbors':'이웃 수',
 'confusion-matrix':'예측 결과','precision-recall':'임계값','roc-auc':'임계값',
 'neural-network':'전달 단계','attention':'단어 가중치','softmax-temperature':'온도',
 'nearest-neighbor-search':'탐색 단계'};
document.getElementById('control-label').textContent=parameterNames[kind];
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const color = (key) => css.getPropertyValue(key).trim();
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const points=Array.from({length:30},(_,i)=>{ const x=.08+.84*rand(); return {x,y:.18+.63*x+(rand()-.5)*.26, cls:x+(rand()-.5)*.65>.52?1:0}; });
const groups=Array.from({length:26},(_,i)=>{ const k=i%3, a=rand()*6.28, r=Math.sqrt(rand())*.16; return {x:[.25,.72,.48][k]+Math.cos(a)*r,y:[.3,.31,.72][k]+Math.sin(a)*r,cls:k}; });
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function axes(){line(X(0),Y(0),X(1),Y(0));line(X(0),Y(0),X(0),Y(1));}
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function polyfit(data,degree){let n=degree+1,A=Array.from({length:n},()=>Array(n+1).fill(0));for(let r=0;r<n;r++){for(let c=0;c<n;c++)A[r][c]=data.reduce((s,p)=>s+Math.pow(p.x,r+c),0);A[r][n]=data.reduce((s,p)=>s+p.y*Math.pow(p.x,r),0);}for(let i=0;i<n;i++){let best=i;for(let r=i+1;r<n;r++)if(Math.abs(A[r][i])>Math.abs(A[best][i]))best=r;[A[i],A[best]]=[A[best],A[i]];let q=A[i][i]||1e-8;for(let c=i;c<=n;c++)A[i][c]/=q;for(let r=0;r<n;r++)if(r!==i){let f=A[r][i];for(let c=i;c<=n;c++)A[r][c]-=f*A[i][c];}}return A.map(row=>row[n]);}
function evalPoly(coefs,x){return coefs.reduce((s,a,i)=>s+a*Math.pow(x,i),0);}
const fitData=[{x:.08,y:.24},{x:.20,y:.42},{x:.32,y:.38},{x:.44,y:.59},{x:.56,y:.55},{x:.68,y:.72},{x:.80,y:.68},{x:.92,y:.83}];
function draw(now){
 const phase=((now-started)%5200)/5200, v=userValue===null ? .16+.74*phase : userValue/100;
 ctx.clearRect(0,0,w,h); let caption='';
 switch(kind){
 case 'feature-engineering': {axes();points.slice(0,16).forEach(p=>dot(p.x,p.y,C.muted,3));let px=points[Math.floor(v*15)];line(X(px.x),Y(0),X(px.x),Y(px.y),C.b,2);dot(px.x,px.y,C.a,6);text('원본: 길이 '+Math.round(px.x*100),X(.04),Y(.92));text('새 특성: 길이² '+Math.round(px.x*px.x*100),X(.04),Y(.79),C.a);caption='원본 값에서 새 열을 계산';break;}
 case 'normalization-scaling': {let vals=[12,35,78,23,61],selected=Math.min(4,Math.floor(v*5));vals.forEach((a,i)=>{let yy=Y(.85-i*.17),norm=(a-12)/66;text(String(a),X(.02),yy+4);bar(X(.18),yy-6,(w-48)*.6*a/80,12,C.muted);bar(X(.18),yy+8,(w-48)*.6*norm,5,i===selected?C.c:C.a);});caption=vals[selected]+' → '+((vals[selected]-12)/66).toFixed(2)+' · MinMax';break;}
 case 'train-test-split': {axes();points.forEach((p,i)=>dot(p.x,p.y,i<Math.round(v*points.length)?C.a:C.b,4));text('학습 '+Math.round(v*30),X(.03),Y(.93),C.a);text('평가 '+(30-Math.round(v*30)),X(.03),Y(.80),C.b);caption='평가 점은 학습에서 제외';break;}
 case 'cross-validation': {let fold=Math.min(4,Math.floor(v*5));for(let r=0;r<5;r++){let yy=Y(.82-r*.16);text(String(r+1),X(.03),yy+4);for(let c=0;c<5;c++)bar(X(.12+c*.16),yy-9,(w-48)*.145,18,c===r?C.b:C.a);if(r===fold){ctx.strokeStyle=C.fg;ctx.lineWidth=2;ctx.strokeRect(X(.11),yy-12,(w-48)*.81,24);}}text('평가 폴드 '+(fold+1),X(.12),Y(.05),C.b);caption='각 행에서 평가 폴드를 교체';break;}
 case 'overfitting': {axes();fitData.forEach(p=>dot(p.x,p.y,C.fg,4));let degree=v>.36?7:1,co=polyfit(fitData,degree);if(degree===7){let simple=polyfit(fitData,1);path(x=>evalPoly(simple,x),C.muted,1,.08,.92);}path(x=>evalPoly(co,x),degree===7?C.b:C.a,3,.08,.92);text(degree===7?'복잡한 곡선':'단순한 직선',X(.05),Y(.9),degree===7?C.b:C.a);caption='훈련 점을 과하게 따르면 흔들림';break;}
 case 'bias-variance': {axes();path(x=>.75-.52*x+.34*x*x,C.a,2);path(x=>.18+.65*x*x,C.b,2);let x=v;line(X(x),Y(0),X(x),Y(1),C.muted);dot(x,.75-.52*x+.34*x*x,C.a,5);dot(x,.18+.65*x*x,C.b,5);text('편향',X(.05),Y(.84),C.a);text('분산',X(.75),Y(.84),C.b);caption='복잡도가 커질수록 균형 이동';break;}
 case 'regularization': {axes();fitData.forEach(p=>dot(p.x,p.y,C.muted,3));let avg=fitData.reduce((s,p)=>s+p.y,0)/fitData.length, slope=.7*(1-v);path(x=>avg+slope*(x-.5),C.a,3);path(x=>avg+.7*(x-.5),C.line,2);text('계수 '+slope.toFixed(2),X(.06),Y(.9),C.a);caption='벌점이 커지면 계수가 작아짐';break;}
 case 'linear-regression': {axes();points.forEach(p=>dot(p.x,p.y,C.muted,3));let slope=.15+.55*v,intercept=.22;path(x=>intercept+slope*x,C.a,3);let mse=points.reduce((s,p)=>s+(p.y-intercept-slope*p.x)**2,0)/points.length;text('평균제곱오차 '+mse.toFixed(3),X(.04),Y(.91),C.a);caption='직선이 오차를 줄이며 이동';break;}
 case 'logistic-regression': {axes();points.forEach(p=>dot(p.x,p.cls? .78:.16,p.cls?C.a:C.b,4));let steep=3+v*13;path(x=>1/(1+Math.exp(-steep*(x-.52))),C.c,3);line(X(.52),Y(0),X(.52),Y(1),C.line);caption='시그모이드로 확률을 추정';break;}
 case 'gradient-descent': {axes();path(x=>.12+2.8*(x-.55)**2,C.a,3);let x=.95-.38*v;dot(x,.12+2.8*(x-.55)**2,C.b,7);line(X(x),Y(.12+2.8*(x-.55)**2),X(x-.08),Y(.12+2.8*(x-.55)**2),C.b,2);caption='기울기 반대 방향으로 한 걸음';break;}
 case 'learning-rate': {axes();path(x=>.12+2.8*(x-.55)**2,C.line,2);let lr=.02+v*.5,x=.95;for(let i=0;i<6;i++){dot(x,.12+2.8*(x-.55)**2,i===5?C.b:C.a,i===5?6:3);let next=x-lr*5.6*(x-.55);line(X(x),Y(.12+2.8*(x-.55)**2),X(Math.max(-1,Math.min(2,next))),Y(.12+2.8*(next-.55)**2),C.a);x=next;}caption='학습률 '+lr.toFixed(2)+' · 이동 폭';break;}
 case 'loss-function': {axes();let slope=.25+.7*v,mse=0;points.slice(0,12).forEach(p=>{let estimate=.18+slope*p.x;mse+=(p.y-estimate)**2;dot(p.x,p.y,C.muted,3);line(X(p.x),Y(p.y),X(p.x),Y(estimate),C.b,2);});path(x=>.18+slope*x,C.a,2);caption='평균제곱오차 '+(mse/12).toFixed(3);break;}
 case 'decision-tree': {axes();let split=.3+.4*v;line(X(split),Y(0),X(split),Y(1),C.a,3);line(X(split),Y(.51),X(1),Y(.51),C.b,3);groups.forEach(p=>dot(p.x,p.y,[C.a,C.b,C.c][p.cls],4));text('x < '+split.toFixed(2),X(.03),Y(.92),C.a);caption='질문으로 영역을 차례로 나눔';break;}
 case 'random-forest': {let votes=[v<.4?0:1,v<.65?0:1,v<.8?1:0],bw=(w-70)/3;votes.forEach((a,i)=>{let xx=28+i*bw;bar(xx,h*.23,bw-12,h*.42,a?C.a:C.b);text('나무 '+(i+1),xx+4,h*.2,C.muted,10);text(a?'A':'B',xx+(bw-12)/2,h*.5,a?'#fff':'#15151a',18,'center');});caption='세 나무 투표 → '+(votes.filter(Boolean).length>=2?'A':'B');break;}
 case 'k-nearest-neighbors': {axes();let q={x:.48,y:.50},sorted=groups.map(p=>({...p,d:Math.hypot(p.x-q.x,p.y-q.y)})).sort((a,b)=>a.d-b.d),k=Math.max(1,Math.round(1+v*8)),radius=sorted[k-1].d;ctx.strokeStyle=C.line;ctx.beginPath();ctx.ellipse(X(q.x),Y(q.y),radius*(w-48),radius*(h-44),0,0,7);ctx.stroke();groups.forEach(p=>dot(p.x,p.y,[C.a,C.b,C.c][p.cls],4));sorted.slice(0,k).forEach(p=>line(X(q.x),Y(q.y),X(p.x),Y(p.y),C.muted));dot(q.x,q.y,C.fg,7);caption='가까운 '+k+'개 이웃의 표결';break;}
 case 'confusion-matrix': {let n=Math.round(8*v);matrix([12+n,3,5,14-n],['예측 양성','예측 음성']);caption='행: 실제 · 열: 예측';break;}
 case 'precision-recall': {let threshold=v,selected=points.filter(p=>p.x>threshold),tp=selected.filter(p=>p.cls).length,actual=points.filter(p=>p.cls).length;axes();points.forEach(p=>dot(p.x,p.cls?.72:.26,p.x>threshold?(p.cls?C.c:C.b):C.muted,4));line(X(threshold),Y(0),X(threshold),Y(1),C.a,2);text('정밀도 '+(selected.length?Math.round(100*tp/selected.length):0)+'%',X(.04),Y(.92),C.c);text('재현율 '+Math.round(100*tp/actual)+'%',X(.55),Y(.92),C.a);caption='임계값 이동 → 두 지표 변화';break;}
 case 'roc-auc': {axes();line(X(0),Y(0),X(1),Y(1),C.line);let samples=points.map(p=>({score:p.x,cls:p.cls})).sort((a,b)=>b.score-a.score),pos=samples.filter(p=>p.cls).length,neg=samples.length-pos;let coords=[[0,0]],tp=0,fp=0;samples.forEach(p=>{if(p.cls)tp++;else fp++;coords.push([fp/neg,tp/pos]);});ctx.strokeStyle=C.a;ctx.lineWidth=3;ctx.beginPath();coords.forEach(([x,y],i)=>i?ctx.lineTo(X(x),Y(y)):ctx.moveTo(X(x),Y(y)));ctx.stroke();let auc=0;for(let i=1;i<coords.length;i++)auc+=(coords[i][0]-coords[i-1][0])*(coords[i][1]+coords[i-1][1])/2;let idx=Math.min(coords.length-1,Math.floor(v*(coords.length-1)));dot(coords[idx][0],coords[idx][1],C.b,7);text('AUC '+auc.toFixed(2),X(.58),Y(.12),C.a);caption='임계값별 TPR · FPR';break;}
 case 'neural-network': {let layers=[3,4,2],pulse=Math.min(1,Math.floor(v*3));layers.forEach((n,l)=>{for(let i=0;i<n;i++){let x=.18+l*.32,y=(i+1)/(n+1);if(l<2){let next=layers[l+1];for(let j=0;j<next;j++)line(X(x),Y(y),X(x+.32),Y((j+1)/(next+1)),l===pulse?C.a:C.line,l===pulse?2:1);}dot(x,y,l<=pulse?C.a:C.surface,7);ctx.strokeStyle=C.a;ctx.beginPath();ctx.arc(X(x),Y(y),7,0,7);ctx.stroke();}});caption='입력 → 은닉층 → 출력';break;}
 case 'attention': {let words=['이','글','의','주제'],weights=[.1,.18,.2+.5*v,.52-.5*v],sum=weights.reduce((a,b)=>a+b,0);words.forEach((word,i)=>{let xx=X(.08+i*.23),hh=(h-55)*weights[i]/sum;bar(xx,Y(0)-hh,Math.max(22,(w-48)*.16),hh,[C.line,C.a,C.b,C.c][i]);text(word,xx+8,Y(0)+14,C.fg,11);});caption='문맥에 따라 단어 가중치가 변함';break;}
 case 'softmax-temperature': {let logits=[2.2,1.4,.4],temp=.25+v*2.5,e=logits.map(x=>Math.exp((x-2.2)/temp)),sum=e.reduce((a,b)=>a+b,0);e.forEach((x,i)=>{let xx=X(.14+i*.28),hh=(h-58)*x/sum;bar(xx,Y(0)-hh,Math.max(25,(w-48)*.17),hh,[C.a,C.b,C.c][i]);text(Math.round(100*x/sum)+'%',xx,Y(0)-hh-5,C.fg,11);});caption='온도 '+temp.toFixed(2)+' · 확률 분포';break;}
 case 'nearest-neighbor-search': {axes();let q={x:.49,y:.49},visited=Math.max(1,Math.round(v*groups.length)),sorted=groups.map((p,i)=>({...p,i,d:Math.hypot(p.x-q.x,p.y-q.y)})),best=sorted.slice(0,visited).sort((a,b)=>a.d-b.d)[0];groups.forEach((p,i)=>dot(p.x,p.y,i<visited?C.muted:C.line,3));dot(best.x,best.y,C.b,7);dot(q.x,q.y,C.a,7);line(X(q.x),Y(q.y),X(best.x),Y(best.y),C.b,2);caption='후보 '+visited+'개 확인 · 현재 최근접';break;}
 }
 status.textContent=caption;
 requestAnimationFrame(draw);
}
slider.addEventListener('input',()=>{userValue=Number(slider.value);});
document.getElementById('restart').addEventListener('click',()=>{started=performance.now();userValue=null;slider.value='55';});
requestAnimationFrame(draw);

Studying the answers before an exam says little about your ability. A train–test split reserves data the fitted model has not seen for a final evaluation.

Random splitting is common; classification may use stratification to preserve class proportions. For time ordered data, train on earlier observations and test on later ones. Fit preprocessing rules on the training portion only.

Duplicates or near identical articles by the same author on both sides can inflate scores. An exploratory text map does not need a predictive test split, but a publisher classifier does.

When to use

Use to claim predictive performance on unseen data; check duplicates, groups, and time order.

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