Accuracy on the test set

$1/1$ point $($graded$)$

After you have chosen your best method $($perceptron$,$ average perceptron or Pegasos$)$ and parameters, use this classifier to compute testing accuracy on the test set.

We have supplied the feature matrix and labels in main.py as test$\_$bow$\_$features and test$\_$labels.

Note: In practice the validation set is used for tuning hyperparameters while a heldout test set is the final benchmark used to compare disparate models that have already been tuned. You may notice that your results using a validation set don't always align with those of the test set, and this is to be expected.

Accuracy on the test set :Accuracy on the test set

$1/1$ point $($graded$)$

After you have chosen your best method $($perceptron$,$ average perceptron or Pegasos$)$ and parameters, use this classifier to compute testing accuracy on the test set.

We have supplied the feature matrix and labels in main.py as test$\_$bow$\_$features and test$\_$labels.

Note: In practice the validation set is used for tuning hyperparameters while a heldout test set is the final benchmark used to compare disparate models that have already been tuned. You may notice that your results using a validation set don't always align with those of the test set, and this is to be expected.

Accuracy on the test set :Accuracy on the test set

$1/1$ point $($graded$)$

After you have chosen your best method $($perceptron$,$ average perceptron or Pegasos$)$ and parameters, use this classifier to compute testing accuracy on the test set.

We have supplied the feature matrix and labels in main.py as test$\_$bow$\_$features and test$\_$labels.

Note: In practice the validation set is used for tuning hyperparameters while a heldout test set is the final benchmark used to compare disparate models that have already been tuned. You may notice that your results using a validation set don't always align with those of the test set, and this is to be expected.

Accuracy on the test set :Accuracy on the test set

$1/1$ point $($graded$)$

After you have chosen your best method $($perceptron$,$ average perceptron or Pegasos$)$ and parameters, use this classifier to compute testing accuracy on the test set.

We have supplied the feature matrix and labels in main.py as test$\_$bow$\_$features and test$\_$labels.

Note: In practice the validation set is used for tuning hyperparameters while a heldout test set is the final benchmark used to compare disparate models that have already been tuned. You may notice that your results using a validation set don't always align with those of the test set, and this is to be expected.

Accuracy on the test set :Accuracy on the test set

$1/1$ point $($graded$)$

After you have chosen your best method $($perceptron$,$ average perceptron or Pegasos$)$ and parameters, use this classifier to compute testing accuracy on the test set.

We have supplied the feature matrix and labels in main.py as test$\_$bow$\_$features and test$\_$labels.

Note: In practice the validation set is used for tuning hyperparameters while a heldout test set is the final benchmark used to compare disparate models that have already been tuned. You may notice that your results using a validation set don't always align with those of the test set, and this is to be expected.

Accuracy on the test set :