Lesson 8: Machine Learning - Classification, Dimesionality Reduction¶
You have already started Machine Learning when you performed the linear regression analysis, but let’s talk about Machine Learning in general first, then, as promised earlier, we’ll move to our larger dataset.
(Learn from data and make decisions)
Use training set with correct inputs and outputs to predict outputs for test data inputs.
- Inputs(X): Features
- Outputs(y): binary or multiple classes
- Inputs(X): Independent Variable
- Outputs(y): Dependent Variable (Continuous)
Find patterns among inputs (features), no labels in data
- Find groups within data (Example: Phylogeny tree)
- Find a lower dimension representation of higher dimensional data
We built a linear regression model in the last lesson, and Classification and Dimensionaloty Reduction component of the ML lesson is currently available as Kaggle Notebook on Tumor Classification between AML and ALL and finding top genes contributing to the classification.
Next, we will explore Clustering.