Feature Engineering Made Easy

Using a linear model on raw pixel values

To begin, we will run the raw pixel values through a logistic regression model in order to obtain something of a baseline model. We want to see if utilizing PCA or RBM components will allow the same linear classifier to perform better or worse. If we can find that the extracted latent features are performing better (in terms of accuracy of our linear model) then we can be sure it is the feature engineering that we are employing that is enhancing our pipeline, and nothing else.

First we will create our instantiated modules:

# import logistic regression and gridsearch module for some machine learning  from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV ...