在非常不平衡的情况下,我正在使用XGboost在Python中进行分类。我知道,根据文档,我可以设置.word {
font-size: 0; /* to remove the uneeded white spaces between the letters */
overflow: hidden;
}
span {
display: inline-block;
font-size: 3rem;
transform: translateX(-400px);
transition: all;
}
span:nth-child(1) { transition-duration: .5s }
span:nth-child(2) { transition-duration: .7s }
span:nth-child(3) { transition-duration: .9s }
span:nth-child(4) { transition-duration: 1.1s }
span:nth-child(5) { transition-duration: 1.3s }
.word.start span {
transform: translateX(0);
}
来处理不平衡,使用以下公式作为经验法则:<div class="word">
<span>H</span>
<span>E</span>
<span>L</span>
<span>L</span>
<span>O</span>
</div>
<button onClick="toggleAnimation()">Toggle animation</button>
。
我想知道是否存在处理成本敏感型学习的规则。例如,我知道积极案例的错误分类会产生很大的影响(比如说100美元),而对否定阶级却有错误(例如10美元)。
在这种情况下如何正确设置highchartsLeaderBoard = Highcharts;
chartOptionsLeaderBoard={
chart: {
reflow:false,
type: 'bar',
marginLeft: 80,
width: 500,
borderWidth:0,
backgroundColor:'transparent',
},
plotOptions: {
bar: {
borderWidth:0,
},
},
title: {
text: 'LeaderBoard',
},
credits:{
enabled:false
},
yAxis: {
visible:false,
title: {
text: ''
}
},
xAxis: {
type: 'category',
min: 0,
labels: {
animate: true,
duration:5000
}
},
legend: {
enabled: false
},
series: [{
zoneAxis: 'x',
zones: [{
value: 1,
color: '#ff4d40'
}],
dataLabels: {
enabled: true,
format: '{y:,.0f}',
borderWidth:0,
color:'white',
style:{
textOutline:0
}
},
dataSorting: {
enabled: true,
sortKey: 'y',
animate: true,
duration:5000
},
data:[],
}]
}
this.chartOptionsLeaderBoard.series[0].data.push(array)
?