机器学习|线性回归三大评价指标实现『MAE, MSE, MAPE』（Python语言描述）
机器学习 / 线性回归
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机器学习|线性回归三大评价指标实现『MAE, MSE, MAPE』（Python语言描述）

对于回归预测结果，通常会有平均绝对误差、平均绝对百分比误差、均方误差等多个指标进行评价。这里，我们先介绍最常用的3个：

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BPR CNN Deep learning ECS Gradient Descent HAC MAE MAPE MF MSE Neural Style Transfer RS Seafile TensorFlow Ubuntu VGG19 artificial neural network bagging blog boosting cluster decision tree deep learning github jekyll k-means k-nearest neighbors linear regression machine learning naive bayes next perceptron polynomial regression python sort support vector machine

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BPR 1
CNN 1
Deep learning 1
ECS 2
Gradient Descent 2
HAC 1
MAE 1
MAPE 1
MF 1
MSE 1
Neural Style Transfer 1
RS 1
Seafile 1
TensorFlow 1
Ubuntu 2
VGG19 1
artificial neural network 1
bagging 1
blog 2
boosting 1
cluster 1
decision tree 1
deep learning 1
github 2
jekyll 1
k-means 2
k-nearest neighbors 1
linear regression 2
machine learning 15
naive bayes 1
next 1
perceptron 1
polynomial regression 1
python 16
sort 2
support vector machine 1

最新文章

归档

标签

BPR 1
CNN 1
Deep learning 1
ECS 2
Gradient Descent 2
HAC 1
MAE 1
MAPE 1
MF 1
MSE 1
Neural Style Transfer 1
RS 1
Seafile 1
TensorFlow 1
Ubuntu 2
VGG19 1
artificial neural network 1
bagging 1
blog 2
boosting 1
cluster 1
decision tree 1
deep learning 1
github 2
jekyll 1
k-means 2
k-nearest neighbors 1
linear regression 2
machine learning 15
naive bayes 1
next 1
perceptron 1
polynomial regression 1
python 16
sort 2
support vector machine 1
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