MACSS

Part 3: Creating and testing photometric redshift estimator

Goals: create a photometric redshift estimator using the scikit-learn tool-kit and test it out

Specifics: I have provided you with some prepared photometric reference data, which includes cross-matched objects with known redshifts. You will want to:

1. prepare the data for consumption by the machine learning algorithms,
2. use part of this data to train a regression model,
3. apply that regression model to remainder of the data
4. investigate how well the regression model performed

Open the notebook nb/Project_Part3.ipynb to find a notebook you can work on this second part of the project in.

If you want to see what things should look like, you can have a look:

1. in the notebook 05_ExploreRedshift.ipynb to see an exploration of the features in the data that can be used to extract redshift information.

2. in the notebook 06_SklearnRegression.ipynb to see examples of running several different types of estimation algorithms.

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