Category : xgbclassifier

I am working on a heavily imbalanced Multi-Class data for classification. I want to use the class_weight as given in many scikit-learn models. What is the best and proper way to do that inside a pipeline. As I have seen in Documentation, scale_pos_weight is for binary classification only. This answer here with 15 upvotes by ..

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I’ve created a binary classification model which predicts whether an article is part of the positive or negative class. I am using TF-IDF fed into an XGBoost classifier alongside another feature. I get an AUC score of very close to 1 when both training/testing and crossvalidating. I got a .5 score when testing on my ..

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I will fully admit this might just be me misunderstanding how it works however; I have used the multi:softprob approach as this is what I assumed was the right fit for my data: [13355 rows x 6 columns] ["Accident_Severity", "Number_of_Vehicles", "Number_of_Casualties", "Speed_limit", "Urban_or_Rural_Area", "Year"] Each column is numeric. Some are simply placeholders for a meaning ..

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In scikit-learn, one can train a single tree on a multiclass problem and use apply() to output the leaves. It does standard, vanilla multiclass classification (not OvR) and will return a Nx1 vector showing one leaf per input. But when I try to do this in XGBoost using n_estimators=1, XGBoost appears to be training a ..

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I am wondering if there is a mistake in the code. I’ve scoured the net and cannot find anything on ‘PreprocTransformer()’. The code is: from sklearn.pipeline import Pipeline pipe = Pipeline([(‘preproc’, PreprocTransformer())]) ]) ————————————————————————— NameError Traceback (most recent call last) <ipython-input-17-7c00a5d2219a> in <module> 7 # Create a pipeline 8 pipe = Pipeline([ —-> 9 (‘preproc’, ..

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I have a dataset like so: print(X_test.dtypes) metric1 int64 rank float64 device_type int8 NA_estimate float64 When I try to make predictions on this data set, I get the following error: y_test_pred_xgb = clf_xgb.predict(xgb.DMatrix(X_test)) TypeError: Not supported type for data.<class ‘xgboost.core.DMatrix’> I searched for a bit but only found discussion of object variable data types causing ..

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