Decision tree for picking ML algorithm
Optimize machine learning workflows with this decision tree example, guiding the selection of algorithms based on data characteristics.
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about this example
Choosing the right ML algorithm is vital for the success of any data-driven project. Here’s a breakdown of this example:
- Labeled Data Decision: Determines the flow based on the presence of labeled data.
- Sample Size Consideration: Differentiates algorithms based on the volume of data samples.
- Data Type Handling: Directs to appropriate algorithms for text data and feature importance.
- Algorithm Selection: Suggests specific ML algorithms like SGD Classifier, Naive Bayes, K-Means Clustering, and Lasso or Ridge Regression.
When to use
- Initial Project Planning: When embarking on a new machine learning project and deciding which algorithm fits the data set.
- Algorithm Optimization: For projects that need to switch algorithms due to changes in data size or type.
- Education and Training: As a teaching aid for software engineers learning about the practical application of machine learning algorithms.
How to use
- Start editing: Duplicate the example file and double-click on the diagram.
- Customize diagram: Add/modify nodes, groups, and relationships using Eraser’s simple diagram-as-code syntax. Incorporate icons for visual ease.
- Customize layout: Drag elements on the diagram directly on the canvas to manipulate the layout.
- Collect feedback: Share the file with collaborators and encourage them to use the comments feature for feedback.
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