Machine Learning

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3. Model linear (II)

PART 1: First use the google play groud to better understanf the impact of regularization

In this lab, we study a cookies data set analyzed via near infrared spectroscopy. Our goal will be to predict the fat percent in the biscuits. Near infrared spectrometry offers a practical alternative to the time-consuming, wet chemical methods and chromatographic techniques. FT-NIR is non-destructive, requiring no sample preparation or hazardous chemicals, making it quick and reliable for quantitative and qualitative analysis. NIR is ideal for rapid raw material identification and is also a powerful analysis tool capable of accurate multi-component quantitative analysis.

In this lab, we continue the study of the cookies dataset. Our goal will be to predict the fat percent in the biscuits.

1. Fit a Ridge regression.
2. Fit a Lasso regression.
3. Use cross validation to compare these two models with the ones obtained in the previous lab.

Codes: CancerRelapse_LinearModel_1.py, CancerRelapse_LinearModel_1.R, Cookies_LinearModel_2_ToStart.py, Cookies_LinearModel_2_ToStart.R