Logistic Regression

For the Logistic Regression ML model we tested four environments, both Red Wine and White Wine: all wine features vs the top 5 features. Based on the features importance test, the top 5 features for Red Wine were alcohol, density, residual sugar, free sulfur dioxide, and pH. The top 5 features for White Wine were alcohol, sulphates, volatile acidity, citric acid, and density.There are two possible predicted classes: "Fair (0)" and "Very Good (1)". When predicting the quality of wine, "very good" would mean to have better quality, and "fair" would mean to lower quality of wine. Based on the test accuracy scores for these models, all features vs the top 5 features was not statistically significant to conclude that one method one was better than the other.

Red Wine

All Features

The test accuracy score for Red Wine considering all features was 0.87

The classifier made a total of 320 predictions. The classifier predicted “Fair (0)” 264 times and predicted “Very Good (1)” 13 times.

Top 5 Features

The test accuracy score for Red Wine considering the top 5 features was 0.86

The classifier made a total of 320 predictions. The classifier predicted “Fair (0)” 263 times and predicted “Very Good (1)” 12 times.

White Wine

All Features

The test accuracy score for White Wine considering all features was 0.79

The classifier made a total of 980 predictions. The classifier predicted “Fair (0)” 707 times and predicted “Very Good (1)” 64 times.

Top 5 Features

The test accuracy score for White Wine considering the top 5 features was 0.79

The classifier made a total of 980 predictions. The classifier predicted “Fair (0)” 718 times and predicted “Very Good (1)” 59 times.