multiclass classification in xgboost (python)
<p>I can't figure out how to pass number of classes or eval metric to xgb.XGBClassifier with the objective function 'multi:softmax'.</p>

<p>I looked at many documentations but the only talk about the sklearn wrapper which accepts n_class/num_class.</p>

<p>My current setup looks like</p>

<pre><code>kf = cross_validation.KFold(y_data.shape[0], \
    n_folds=10, shuffle=True, random_state=30)
err = [] # to hold cross val errors
# xgb instance
xgb_model = xgb.XGBClassifier(n_estimators=_params['n_estimators'], \
    max_depth=params['max_depth'], learning_rate=_params['learning_rate'], \
    min_child_weight=_params['min_child_weight'], \
    subsample=_params['subsample'], \
    colsample_bytree=_params['colsample_bytree'], \
    objective='multi:softmax', nthread=4)

# cv
for train_index, test_index in kf:
    xgb_model.fit(x_data[train_index], y_data[train_index], eval_metric='mlogloss')
    predictions = xgb_model.predict(x_data[test_index])
    actuals = y_data[test_index]
    err.append(metrics.accuracy_score(actuals, predictions))
</code></pre>