Your perceptron$-$like algorithm is unable to reach zero errors on your training data. Which of the following techniques would help improve performance on the training data?

Add higher$-$order features to our list of six features, e$.$g$.,$ pairwise products, and run our perceptron algorithm with this newly constructed dataset. New features increase the dimensionality of the space, and improve the chances that the data is separable.

Collect a larger set of data, so the perceptron algorithm does a better job of fitting to the data distribution; with a small training set, the perceptron cannot fully learn $w,$ causing it to produce errors on the training data.

Removing some of the features from the training data, and training the perceptron on this subset of data. Too many features increases the chance of overfitting on the training data, which would decrease performance on the training data.

Running the perceptron algorithm for a longer period of time; since the perceptron algorithm is guaranteed to keep equal or reduce the number of errors at each time step, we are guaranteed to eventually reach zero errors.