title: Hyperparameters tuning with keras tuner for classification problem
body: <p>I an trying implement both classification problem and the regression problem with Keras tuner. here is my code for the regression problem:</p>
  <pre><code>    def build_model(hp):
          model = keras.Sequential()
          for i in range(hp.Int('num_layers', 2, 20)):
              model.add(layers.Dense(units=hp.Int('units_' + str(i),
                                                  min_value=32,
                                                  max_value=512,
                                                  step=32),
                                     activation='relu'))
              if hp.Boolean(&quot;dropout&quot;):
                model.add(layers.Dropout(rate=0.5))
          # Tune whether to use dropout.
      
          model.add(layers.Dense(1, activation='linear'))
          model.compile(
              optimizer=keras.optimizers.Adam(
                  hp.Choice('learning_rate', [1e-4, 1e-3, 1e-5])),
              loss='mean_absolute_error',
              metrics=['mean_absolute_error'])
          return model
  tuner = RandomSearch(
      build_model,
      objective='val_mean_absolute_error',
      max_trials=5,
      executions_per_trial=2,
      # overwrite=True,
      directory='projects',
      project_name='Air Quality Index')
  </code></pre>
  <p>In order to apply this code for a classification problem, which parameters(loss, objective, metrices etc.) have to be changed?</p>