In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

- Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
- Support Vector Machine: e.g., the optimal choice of Kernel Functions
- Ensemble: e.g., the number of Learning Cycles for Bagging.
- Discriminant Analysis: e.g., Linear/Quadratic; regularization choices for covariance matrix.
- Naïve Bayes: e.g., Kernel choices; bandwidth selections.
- K-nearest Neighbours: e.g., Distance metrics; k in kNN.
- Decision Tree: e.g., Impurity measure choices; Tree Size Constraint.
- Logistic Regression

In the following paper, we discuss in details how the parameterization choices are made in the context of Financial Market and the parameters are tuned in order to achieve optimal performance for each classifier mentioned above: the paper is available: https://ssrn.com/abstract=2967184 ; the presentation slide gives a summary: https://ssrn.com/abstract=2973065

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central