Questions tagged [xgbclassifier]

112 questions
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multiclass CV in XGboost (python) - some classes not in train/validation sub-groups

I am working with an XGboost model in python, with a large dataset comprised of embeddings (x) and corresponding labels (y), I have about 30000 samples. The data is very imbalanced, with 8 different classes of labels. I am attempting to perform…
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XGBoost: How to calculate leaf weights in multi-class classification

I have trouble understanding how XGBoost calculates the leaf weights in multi-class classification. I generated a simple example with two features and 3 classes. The training data looks like this: feature 0 feature 1 label…
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Invalid Parameter XGB For Estimator

I am performing a GridSearch on the parameters below for an XGB Classifier. It's simple enough, but when I run grid_search.fit(X_train, y_train) it returns ValueError: Invalid parameter xgb for estimator I completed a xgb.get_params() to confirm the…
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Proper way to use 'class_weight' for Multiclass Classification in XGBoostClassifier( ) in a Pipeline

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,…
Deshwal
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Describe or display the relationship between variables and the labels xgboost?

I have a model: model<-xgboost(data=as.matrix(data[,-1]),label=data$Ethnicity, num_class=8, nrounds=50,objective="multi:softmax",lambda=1, eval_metric="merror") data is a matrix of 94 variables of random survey question and the label is Ethnicity…
user16596066
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Cross validation returning 0 for train-merror and test-merror

Here is a sample from my data: data ## q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 q18 q19 q20 q21 q22 q23 w ## 1 1.73 54.43 2 5 5 1 1 1 1 1 1 2 3 2 2 2 2 2 0 ## 2 1.50 51.26 2 5 1 1 1 1 …
user16596066
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How to set SageMaker xgboost's eval_metric to f1?

I tried SageMaker's AutoPilot to solve a binary classification problem and I found it is using f1 as the evaluation metric. But when I tried to write some code without tuning like this: xgb.set_hyperparameters(max_depth=5, …
kee
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How to solve XGBoost error for traceback and unknown objective function?

I was trying to build an XGBoost Binary Classification model. I set up my training and test data and performed the following action to fit the data into the model. clf_xgb = xgb.XGBClassifier(objective = 'binary: logistic', missing = None, seed =…
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XGBoost for python is not accepting the shapes of my target

I have been struggling to get python's xgboost to accept targets due to their shape (I have tried shapes of (-1,1) vs (-1,), format of pandas vs numpy, and LabelBinarizing vs One-hot-encoding the target). Any advice to get this going? import numpy…
Anthony M
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TypeError with Sklear XGBoost and Sklearn Calibrated Classifier CV

I'm trying to calibrate the output probabilities from a XGBClassifier. I've provided the sample code, x_train, x_test, y_train, y_test = train_test_split(x_var, y_var, test_size = 0.2, shuffle = False) new_mod2 = xgb.XGBClassifier(scale_pos_weight =…
Redratz
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Titanic Dataset overfitting: can it be that much?

I am a bit confused as I am training a model that yields circa 88% CV score on the train data while the same model performs poorly on the test data after I submit it ( score of 0.75). This drop of 12 points in accuracy can't be all due to…
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use of base_score parameter in R for XGBoost multiclass problem

Im trying to understand how a xgboost works for a multiclass problem. I have used the IRIS dataset to predict which species an input belongs to based on its characteristics and computed results in R. The code is below test <-…
akshay
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printing out Best Parameter with Max/Best AUC Score

I am doing hyperparameter tuning and I am unable to print out max AUC score together with best parameter. see my below code. It only print 'Best params: None, AUC: inf' instead of 'Best params: 5, AUC: 87.1' #HyperParameter Tuning gridsearch_params…
Tumi Sebela
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Predict function giving error for XGBoost but running for GBM in H2O

I am making a classification model using H2O in python. I am able to build a GBM model and make predictions on training and test dataset whereas when I build an XGBoost model and try to make predictions. Below is the GBM code: (Runs perfectly…
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why the value of fscore is greater than 1?

I used XGBClassifier to fit the dataset and got feature_score using codes below: feature_score = clf.get_booster().get_score() The fscores i got all greater than 1 but how can that possible?
bulala
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