2020年1月4日 · $\begingroup$ @dmartin: Thank you for you upvote but apologies as I somewhat disagree with the point you make. . Unless we are looking at a severely imbalanced problem a performance degradation in terms of AUC-ROC from 90% down to 68% is extremely unlikely to be due to "moderate discrepancies" in the train-test (T-T) s

This makes sense, since as others have said passing in a watchlist to xgb.cv doesn't make a ton of sense. So the "test" showing in your cv output is not the same data set as the "test" showing in your xgb.train output. xgb.train calls xgb.iter.eval in order to evaluate the test statistics of the in-sample and watchlist data.

Using the Boston housing dataset as example, I'm comparing the Regression Coefficients between Sklearn's LinearRegression() and xgboost's XGBRegressor(). For XGBRegressior, I'm using booser='gbli...

2018年2月1日 · I am a newbie to Xgboost and I would like to use it for regression, in particular, car prices prediction. I started following a tutorial on XGboost which uses XGBClassifier and objective= 'binary:

2019年2月27日 · I have a total of around 35 features after some feature engineering and the features I have are mostly continuous variables. I tried fitting a Logistic Model, an RF model and and XGB Model. They all seem to give me the same performance. My understanding is that XGB Models generally fare a little better than Logistic Models for these kind of ...

2024年1月19日 · from sklearn.datasets import make_classification import numpy as np import xgboost as xgb # Make a synthetic ranking dataset for demonstration seed = 1994 X, y = make_classification(random_state=seed) rng = np.random.default_rng(seed) n_query_groups = 1 qid = rng.integers(0, n_query_groups, size=X.shape[0]) # Sort the inputs based on query ...

I am reading through Chen's XGBoost paper. He writes that during the $\text{t}^{\text{th}}$ iteration, the objective function below is minimised.

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2019年8月16日 · Like with random forests, there are different ways to compute the feature importance. In XGBoost, which is a particular package that implements gradient boosted trees, they offer the following ways for computing feature importance:

2020年3月30日 · I am using Scikit-Learn XGBClassifier API with sample weights. If I multiply sample weights by 2, I get totally different results with exact same parameters and random_state, I am expecting that If...