Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

Introduction
Data
    Sloan Digital Sky Survey - Spectral Data
        Broad-Band Photometry - Observed
        Medium-Band Photometry - Synthetic
    Pre-Processing 
Methods 
    Input Features and Target Variables in ML Model 
    ML Algorithm 
    Model Optimization
    Evaluation Metrics for Model Performance
Results
    Comparison between True and Predicted Values
    Input Feature Importance
    Effect of Errors in Input Features
Discussion 
Summary 
Acknowledgments
References

• Hosung Lim(Department of Astronomy & Atmospheric Sciences, Kyungpook National University, Daegu 41566, Republic of Korea)
• Hyunjin Shim(Department of Earth Science Education, Kyungpook National University, Daegu 41566, Republic of Korea) Corresponding author
• Myungshin Im(SNU Astronomy Research Center, Astronomy Program, Department of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of Korea)
• Ji Hoon Kim(SNU Astronomy Research Center, Astronomy Program, Department of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of Korea)
• Seong-Kook Lee(SNU Astronomy Research Center, Astronomy Program, Department of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of Korea)
• Gregory S. H. Paek(SNU Astronomy Research Center, Astronomy Program, Department of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of Korea, Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USA)
• Eunhee Ko(SNU Astronomy Research Center, Astronomy Program, Department of Physics & Astronomy, Seoul National University, Seoul 08826, Republic of Korea)
• Dohyeong Kim(Department of Earth Sciences, Pusan National University, Busan 46241, Republic of Korea)