.

Author: mapattacker

evaluation.rst

@@ -823,4 +823,15 @@ https://automl.github.io/auto-sklearn/master/
     automl.fit(X_train, y_train)
 
     y_pred = automl.predict(X_test)
-    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_pred))
+    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_pred))
+
+
+**Auto Keras** uses neural network for training. Similar to Google’s AutoML approach.
+
+.. code:: python
+
+    import autokeras as ak
+
+    clf = ak.ImageClassifier()
+    clf.fit(x_train, y_train)
+    results = clf.predict(x_test)

supervised.rst

@@ -913,70 +913,6 @@ Parameters include
   Accuracy of NN classifier on training set: 0.98
   Accuracy of NN classifier on test set: 0.97
 
-Auto-ML
-********
-Quite a number of open-sourced automatic machine learning packages have been released.
-It selects the best models and their hyperparameters, making it extremely easy to train supervised models.
-
-Auto Keras
-^^^^^^^^^^^
-Uses neural network for training. Similar to Google's AutoML approach.
-
-.. code:: python
-
-  import autokeras as ak
-
-  clf = ak.ImageClassifier()
-  clf.fit(x_train, y_train)
-  results = clf.predict(x_test)
-
-
-Auto Sklearn
-^^^^^^^^^^^^^
-Using Bayesian optimizer, this automation trains using models within sklearn.
-
-.. code:: python
-
-  import autosklearn.classification
-  import sklearn.model_selection
-  import sklearn.datasets
-  import sklearn.metrics
-
-  X, y = sklearn.datasets.load_digits(return_X_y=True)
-  X_train, X_test, y_train, y_test = \
-          sklearn.model_selection.train_test_split(X, y, random_state=1)
-
-  # time_left_for_this_task (total) must be more than per_run_time_limit
-  automl = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=60, 
-                                                            per_run_time_limit=30)
-  automl.fit(X_train, y_train)
-  y_hat = automl.predict(X_test)
-  print("Accuracy score", sklearn.metrics.accuracy_score(y_test, y_hat))
-
-We can see the total results
-
-.. code:: python
-
-  print(automl.sprint_statistics())
-
-  # auto-sklearn results:
-  #   Dataset name: cb9e4c17575300e0bcdb85c0920f3385
-  #   Metric: accuracy
-  #   Best validation score: 1.000000
-  #   Number of target algorithm runs: 51
-  #   Number of successful target algorithm runs: 47
-  #   Number of crashed target algorithm runs: 3
-  #   Number of target algorithms that exceeded the time limit: 1
-  #   Number of target algorithms that exceeded the memory limit: 0
-
-
-
-
-Auto WEKA
-^^^^^^^^^^
-WEKA is a GUI-based software for easy quick analysis of datasets.
-It is the same in concept as Auto Sklearn but have a wider range of models
-and hyperparameters.
 
 |
 Regression