CODEWITHSUNDEEP
pythonscikit-learnxgboost
Jonathan Bechtel
Jonathan Bechtel

Reputation: 3607

Using Custom Metric for Score Method in XGBoost

I am using xgboost for a classification problem with an imbalanced dataset. I plan on using some combination of an f1-score or roc-auc as my primary criteria for judging the model.

Currently the default value returned from the score method is accuracy, but I would really like to have a specific evaluation metric returned instead. My big motivation for doing this is that I presume the feature_importances_ attribute from the model is determined from what's affecting the score method, and the columns that impact predictive accuracy might very well be different from the columns that impact roc-auc. Right now I am passing in values to eval_metric but it does not seem to be making a difference.

Here is some sample code:

from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import roc_auc_score

data = load_breast_cancer()
X = data['data']
y = data['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2, stratify=y)

mod.fit(X_train, y_train)

Now at this point, mod.score(X_test, y_test) will return a value of ~ 0.96, and the roc_auc_score is ~ 0.99.

I was hoping the following snippet:

mod.fit(X_train, y_train, eval_metric='auc')

Would then allow mod.score(X_test, y_test) to return the roc_auc_score value, but it is still returning predictive accuracy, not roc_auc.

The purpose of this exercise is estimating the influence of different columns on the outcome, so if I could get feature_importances_ returned using f1 or roc_auc as the measure of impact this would be a huge boon, but I do not seem to be on the right path as of now.

Thank you.

Upvotes: 1

Views: 1592

Answers (1)

StupidWolf
StupidWolf

Reputation: 46888

There are two parts to your question, to use eval_metric, you need to provide data to evaluate using eval_set = :

mod = XGBClassifier()
mod.fit(X_train, y_train,eval_set=[(X_test,y_test)],eval_metric="auc")

You can check the auc using evals_result(), and it gives the auc for every iteration:

mod.evals_result()

{'validation_0': OrderedDict([('auc',
               [0.965939,
                0.9833,
                0.984788,
                [...]
                0.991402,
                0.991071,
                0.991402,
                0.991733])])}

The importance score is calculated based on the average gain across all splits the feature is used in see help page. From your question, I suppose you need the mdoel to maximize auc, like in cross-validation, but you cannot use the auc as an objective in xgboost. Gradient boosting methods require a differentiable loss function.

With imbalanced dataset, you can try to adjust the parameter scale_pos_weight, to adjust the balance of positive and negative weights. This is discussed in xgboost website

Upvotes: 1

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