I am building a model for binary classification problem where each of my data points is of 300 dimensions (I am using 300 features). I am using a PassiveAggressiveClassifier from sklearn. The model is performing really well.

I wish to plot the decision boundary of the model. How can I do so?

To get a sense of the data, I am plotting it in 2D using TSNE. I reduced the dimensions of the data in 2 steps - from 300 to 50, then from 50 to 2 (this is a common recommendation). Below is the code snippet for the same :

from sklearn.manifold import TSNE

from sklearn.decomposition import TruncatedSVD

X_Train_reduced = TruncatedSVD(n_components=50, random_state=0).fit_transform(X_train)

X_Train_embedded = TSNE(n_components=2, perplexity=40, verbose=2).fit_transform(X_Train_reduced)

#some convert lists of lists to 2 dataframes (df_train_neg, df_train_pos) depending on the label -

#plot the negative points and positive points

scatter(df_train_neg.val1, df_train_neg.val2, marker='o', c='red')

scatter(df_train_pos.val1, df_train_pos.val2, marker='x', c='green')

I get a decent graph.

Is there a way that I can add a decision boundary to this plot which represents the actual decision boundary of my model in the 300 dim space?