pycaret模型分析之绘制模型结果
分析训练完成的机器学习模型的性能是任何机器学习工作流程中必不可少的步骤。 在PyCaret中分析模型性能就像编写plot_model一样简单。 该函数将受训的模型对象和图的类型作为plot_model函数中的字符串。
分类:
Name |
Plot |
---|---|
Area Under the Curve |
‘auc’ |
Discrimination Threshold |
‘threshold’ |
Precision Recall Curve |
‘pr’ |
Confusion Matrix |
‘confusion_matrix’ |
Class Prediction Error |
‘error’ |
Classification Report |
‘class_report’ |
Decision Boundary |
‘boundary’ |
Recursive Feature Selection |
‘rfe’ |
Learning Curve |
‘learning’ |
Manifold Learning |
‘manifold’ |
Calibration Curve |
‘calibration’ |
Validation Curve |
‘vc’ |
Dimension Learning |
‘dimension’ |
Feature Importance |
‘feature’ |
Model Hyperparameter |
‘parameter’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
diabetes = get_data('diabetes')
# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = diabetes, target = 'Class variable')
# creating a model
lr = create_model('lr')
# plotting a model
plot_model(lr)
回归:
Name |
Plot |
---|---|
Residuals Plot |
‘residuals’ |
Prediction Error Plot |
‘error’ |
Cooks Distance Plot |
‘cooks’ |
Recursive Feature Selection |
‘rfe’ |
Learning Curve |
‘learning’ |
Validation Curve |
‘vc’ |
Manifold Learning |
‘manifold’ |
Feature Importance |
‘feature’ |
Model Hyperparameter |
‘parameter’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
boston = get_data('boston')
# Importing module and initializing setup
from pycaret.regression import *
reg1 = setup(data = boston, target = 'medv')
# creating a model
lr = create_model('lr')
# plotting a model
plot_model(lr)
聚类:
Name |
Plot |
---|---|
Cluster PCA Plot (2d) |
‘cluster’ |
Cluster TSnE (3d) |
‘tsne’ |
Elbow Plot |
‘elbow’ |
Silhouette Plot |
‘silhouette’ |
Distance Plot |
‘distance’ |
Distribution Plot |
‘distribution’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
jewellery = get_data('jewellery')
# Importing module and initializing setup
from pycaret.clustering import *
clu1 = setup(data = jewellery)
# creating a model
kmeans = create_model('kmeans')
# plotting a model
plot_model(kmeans)
异常检测:
Name |
Plot |
---|---|
t-SNE (3d) Dimension Plot |
‘tsne’ |
UMAP Dimensionality Plot |
‘umap’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
anomalies = get_data('anomaly')
# Importing module and initializing setup
from pycaret.anomaly import *
ano1 = setup(data = anomalies)
# creating a model
iforest = create_model('iforest')
# plotting a model
plot_model(iforest)
自然语言处理:
Name |
Plot |
---|---|
Word Token Frequency |
‘frequency’ |
Word Distribution Plot |
‘distribution’ |
Bigram Frequency Plot |
‘bigram’ |
Trigram Frequency Plot |
‘trigram’ |
Sentiment Polarity Plot |
‘sentiment’ |
Part of Speech Frequency |
‘pos’ |
t-SNE (3d) Dimension Plot |
‘tsne’ |
Topic Model (pyLDAvis) |
‘topic_model’ |
Topic Infer Distribution |
‘topic_distribution’ |
Word cloud |
‘wordcloud’ |
UMAP Dimensionality Plot |
‘umap’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
kiva = get_data('kiva')
# Importing module and initializing setup
from pycaret.nlp import *
nlp1 = setup(data = kiva, target = 'en')
# creating a model
lda = create_model('lda')
# plotting a model
plot_model(lda)
关联规则挖掘:
Plot |
Abbrev. String |
---|---|
Support, Confidence and Lift (2d) |
‘frequency’ |
Support, Confidence and Lift (3d) |
‘distribution’ |
例子:
# Importing dataset
from pycaret.datasets import get_data
france = get_data('france')
# Importing module and initializing setup
from pycaret.arules import *
arul1 = setup(data = france, transaction_id = 'Invoice', item_id = 'Description')
# creating a model
model = create_model(metric = 'confidence')
# plotting a model
plot_model(model)
- Effective Modern C++翻译(5)-条款4:了解如何观察推导出的类型
- Effective Modern C++翻译(4)-条款3:了解decltype
- 大白话-constructor
- Effective Modern C++翻译(3)-条款2:明白auto类型推导
- React Native在Android平台运行gif的解决方法
- Effective Modern C++翻译(2)-条款1:明白模板类型推导
- Android ormLite复杂条件查询
- Effective Modern C++翻译(1):序言
- C++操作mysql方法总结(2)
- Linux基础(day3)
- C++操作mysql方法总结(1)
- javascript实现最基本、最简单的继承
- C++操作mysql方法总结(3)
- 8.5 输入输出重定向
- JavaScript 教程
- JavaScript 编辑工具
- JavaScript 与HTML
- JavaScript 与Java
- JavaScript 数据结构
- JavaScript 基本数据类型
- JavaScript 特殊数据类型
- JavaScript 运算符
- JavaScript typeof 运算符
- JavaScript 表达式
- JavaScript 类型转换
- JavaScript 基本语法
- JavaScript 注释
- Javascript 基本处理流程
- Javascript 选择结构
- Javascript if 语句
- Javascript if 语句的嵌套
- Javascript switch 语句
- Javascript 循环结构
- Javascript 循环结构实例
- Javascript 跳转语句
- Javascript 控制语句总结
- Javascript 函数介绍
- Javascript 函数的定义
- Javascript 函数调用
- Javascript 几种特殊的函数
- JavaScript 内置函数简介
- Javascript eval() 函数
- Javascript isFinite() 函数
- Javascript isNaN() 函数
- parseInt() 与 parseFloat()
- escape() 与 unescape()
- Javascript 字符串介绍
- Javascript length属性
- javascript 字符串函数
- Javascript 日期对象简介
- Javascript 日期对象用途
- Date 对象属性和方法
- Javascript 数组是什么
- Javascript 创建数组
- Javascript 数组赋值与取值
- Javascript 数组属性和方法