data310
Informal Responses for Monday 7/12/21
Which of the two models produces a better loss metric (see this link for an explanation of the loss function). Produce a plot that supports your answer.
For the first set of models, I used highway-mpg as target and num-of-cylinders, engine-size, horsepower, curb-weight as the features. When using multi-class linear regression, the mean absolute error for this model was around 2.051928. When using multi-class DNN regression, the mean absolute error for this model was around 3.362609. Due to the model created by the usage of multi-class linear regression having a lower mean absolute error, I would say that this model produces a better loss metric.
Graphs for First Set of Models
Linear Model
DNN Model
Return to the remainder of the variables from the dataset and add additional continuous and categorical features with the intent of improving your loss metric. Produce a plot that demonstrates the value of your model. What is the best model your team was able to produce?
Second Set of Models
For the second set of models, I used highway-mpg as target and num-of-cylinders, engine-size, horsepower, curb-weight, fuel-type, price, wheel-base, fuel-system as the features. When using multi-class linear regression, the mean absolute error for this model was around 3.570920. When using multi-class DNN regression, the mean absolute error for this model was around 2.094814.
Linear Model
DNN Model
Third Set of Models
For the third set of models, I used highway-mpg as target and num-of-cylinders, engine-size, horsepower, curb-weight, num-of-doors, peak-rpm, bore, body style as the features. When using multi-class linear regression, the mean absolute error for this model was around 2.557662. When using multi-class DNN regression, the mean absolute error for this model was around 2.494790.
Linear Model
DNN Model
Fourth Set of Models
For the fourth set of models, I used highway-mpg as target and num-of-cylinders, engine-size, horsepower, curb-weight, engine location, compression ratio, stroke, aspiration as the features. When using multi-class linear regression, the mean absolute error for this model was around 1.609359. When using multi-class DNN regression, the mean absolute error for this model was around 1.893565.
Linear model
DNN Model
