title: How to implement t-SNE in a model?
body: <p>I split my data to train/test.
  When i use PCA It is straight forward.</p>
  
  <pre><code>from sklearn.decomposition import PCA
  pca = PCA()
  X_train_pca = pca.fit_transform(X_train)
  X_test_pca = pca.transform(X_test)
  </code></pre>
  
  <p>From here i can use X_train_pca and X_test_pca in the next step and so on..</p>
  
  <p>But when i use t-SNE</p>
  
  <pre><code>from sklearn.manifold import TSNE
  X_train_tsne = TSNE(n_components=2, random_state=0).fit_transform(X_train)
  </code></pre>
  
  <p>I can't seem to transform the test set so that i can use the t-SNE data for the next step e.g. SVM.</p>
  
  <p>Any help?</p>