Given a machine learning problem, the first question many people ask is usually: what is the best classification/regression algorithm to solve it? However, there is no one-size-fits-all solution or free lunch. No one could know which algorithm will work the best before trying multiple methods and fine-tuning the optimal one. We will be looking into best practices around this in the following sections.
Best practices in the model training, evaluation, and selection stage
by Yuxi [Hayden] Liu
Python Machine Learning By Example
- Credits
- Preface
- Getting Started with Python and Machine Learning
- What is machine learning and why do we need it?
- A very high level overview of machine learning
- A brief history of the development of machine learning algorithms
- Generalizing with data
- Overfitting, underfitting and the bias-variance tradeoff
- Avoid overfitting with feature selection and dimensionality reduction
- Preprocessing, exploration, and feature engineering
- Combining models
- Installing software and setting up
- Troubleshooting and asking for help
- Summary
- Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
- Spam Email Detection with Naive Bayes
- News Topic Classification with Support Vector Machine
- Click-Through Prediction with Tree-Based Algorithms
- Click-Through Prediction with Logistic Regression
- Stock Price Prediction with Regression Algorithms
- Best Practices
- Machine learning workflow
- Best practices in the data preparation stage
- Best practices in the training sets generation stage
- Best practice 5 - determine categorical features with numerical values
- Best practice 6 - decide on whether or not to encode categorical features
- Best practice 7 - decide on whether or not to select features and if so, how
- Best practice 8 - decide on whether or not to reduce dimensionality and if so how
- Best practice 9 - decide on whether or not to scale features
- Best practice 10 - perform feature engineering with domain expertise
- Best practice 11 - perform feature engineering without domain expertise
- Best practice 12 - document how each feature is generated
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
Best practices in the model training, evaluation, and selection stage
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