Multiclass classification with xgboost classifier?
Solution 1
By default, XGBClassifier uses the objective='binary:logistic'
. When you use this objective, it employs either of these strategies: one-vs-rest
(also known as one-vs-all) and one-vs-one
. It may not be the right choice for your problem at hand.
When you use objective='multi:softprob'
, the output is a vector of number of data points * number of classes. As a result, there is an increase in time complexity of your code.
Try setting objective=multi:softmax
in your code. It is more apt for multi-class classification task.
Solution 2
In fact, even if the default obj parameter of XGBClassifier
is binary:logistic
, it will internally judge the number of class of label y. When the class number is greater than 2, it will modify the obj parameter to multi:softmax
.
https://github.com/dmlc/xgboost/blob/master/python-package/xgboost/sklearn.py
class XGBClassifier(XGBModel, XGBClassifierBase):
# pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
def __init__(self, objective="binary:logistic", **kwargs):
super().__init__(objective=objective, **kwargs)
def fit(self, X, y, sample_weight=None, base_margin=None,
eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable = attribute-defined-outside-init,arguments-differ
evals_result = {}
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
xgb_options = self.get_xgb_params()
if callable(self.objective):
obj = _objective_decorator(self.objective)
# Use default value. Is it really not used ?
xgb_options["objective"] = "binary:logistic"
else:
obj = None
if self.n_classes_ > 2:
# Switch to using a multiclass objective in the underlying
# XGB instance
xgb_options['objective'] = 'multi:softprob'
xgb_options['num_class'] = self.n_classes_
Solution 3
By default,XGBClassifier or many Classifier uses objective as binary but what it does internally is classifying (one vs rest) i.e. if you have 3 classes it will give result as (0 vs 1&2).If you're dealing with more than 2 classes you should always use softmax.Softmax turns logits into probabilities which will sum to 1.On basis of this,it makes the prediction which classes has the highest probabilities.As you can see the complexity increase as Saurabh mentioned in his answer so it will take more time.
user_12
Updated on June 25, 2020Comments
-
user_12 almost 4 years
I am trying out multi-class classification with xgboost and I've built it using this code,
clf = xgb.XGBClassifier(max_depth=7, n_estimators=1000) clf.fit(byte_train, y_train) train1 = clf.predict_proba(train_data) test1 = clf.predict_proba(test_data)
This gave me some good results. I've got log-loss below 0.7 for my case. But after looking through few pages I've found that we have to use another objective in XGBClassifier for multi-class problem. Here's what is recommended from those pages.
clf = xgb.XGBClassifier(max_depth=5, objective='multi:softprob', n_estimators=1000, num_classes=9) clf.fit(byte_train, y_train) train1 = clf.predict_proba(train_data) test1 = clf.predict_proba(test_data)
This code is also working but it's taking a lot of time to complete compared when to my first code.
Why is my first code also working for multi-class case? I have checked that it's default objective is binary:logistic used for binary classification but it worked really well for multi-class? Which one should I use if both are correct?