MLProcess
2019-09-16 12:04:29 25 举报AI智能生成
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抱歉，暂无相关内容 机器学习基本流程图
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Process
Introduction
Project background
Data source
Else
Define Problem
Supervise or unsupervise
Classification or regression
Data size
Library Loading
Common library
Visualiaztion library
Preprocessing library
Feature Engineering library
Model library
Data Loading
Load train and test
Define function to divide and combind train and test
Contact dataframe if necessary
Basic Info Showing for Train and Test
Null data size for every features
Anomaly data size
Independent anomaly
Min and max
Aggregation anomaly
Outlier
Context anomaly
Data Visualization
Distribution
Continuous feature
Wave figure
Line figure
Categorical feature
Bar figure
Relation
Scatter figure
Data Preprocessing
Dealing with missing data
Dealing with anomaly data
Continuous feature discretization
Label encoding
One-Hot encoding
Continuous feature data Transform
Normalization
Feature Engineering
Build new feature with old features and something else infomation
Feature selection
Feature variance
Relevance with target feature
Model and Blend
Single model
Multi model
Blend models
Simple weight to models
Meta model to blend other models
Some Little Trick
Correction factor
Single factor
Multi factor
Model stacking
Data resample
Model Evaluate
Pipeline and Persistence
Summary and Look Back
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