Eventually, I compiled over 20 Machine Learning-related cheat sheets. Some I reference frequently and thought others may benefit from them too. Machine learning algorithm cheat sheet for.
- Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.
- The machine learning algorithm cheat sheet The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. This article walks you through the process of how to use the sheet.
- The MicrosoftML: Algorithm Cheat Sheet helps you choose the right machine learning algorithm for a predictive analytics model when using Machine Learning Server. The algorithms are available in R or Python. MicrosoftML provides a library of algorithms from the regression, classification (two-class and multi-class), and anomaly detection families.
Key points about understanding the cheat sheet –
Computer Learning Cheat Sheets
- The suggestions in the cheat sheet are approximate rules-of-thumb
- This cheat sheet is intended to suggest a starting point
- Run a head-to-head competition between several algorithms on your data
- Each machine learning algorithm has its own style or inductive bias
- For a specific problem, several algorithms may be appropriate and one algorithm may be a better fit than others
- It’s not always possible to know beforehand which is the best fit
- An appropriate strategy would be to try one and if the results are not satisfactory, try the others