Questions tagged [xgboost]

For questions related to the eXtreme Gradient Boosting algorithm.

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1answer
316 views

How to forecast product/item sales next one week using Xgboost regressor

I have trained xgboost algorithm to predict the number of items sale on a given day and got pretty good results, now I would like to forecast sales ahead of one week. I tried re-training the ...
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2answers
4k views

Python XGBoost predict_proba returns very high or low probabilities

I trained my data with XGBoost in python with GridSearchCV as follows: ...
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0answers
34 views

I have tried 5 different types of model but all returns really low training accuracy (~64%) and low testing accuracy (~14%). What should I do?

I am working with a typical regressor problem. There are $6$ features in the dataset that I am concerned with. There are about $800$ data points in my dataset. The features and the predicted values ...
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2answers
38 views

Cost function - ideas

I build xgboost model for regression problem. By the default xgboost optimize $(y - y_{pred})^2$, so the RMSE will be the best eval metric to measure performance. But my task is to build the best ...
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0answers
44 views

How to test model accuracy on new vs. historical data?

I created an XG Boost model to predict churn using a dataset of customers who were sold during 2018. The accuracy of the model is 89%. Does it make more sense to re-pull the 2018 dataset, where more ...
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0answers
52 views

Feature Importance Scores from Gradient Boosting vs Random Forest

In sklearn, the feature_importances_ attribute exists for both RandomForestClassifier and GradientBoostingClassifier. Would like to know what are the fundamental differences in how this attribute is ...
3
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2answers
418 views

Tweedie Loss for Keras

We are currently using XGBoost model with Tweedie loss for solving a regression problem which works very good, now I wanted to move our model to Keras and experience with neural networks, do anybody ...
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3answers
798 views

Xgboost Parameter Tuning

Are there methods to tune and train an xgboost model in an optimized time - when I tune paramaters and train the model it takes around 12 hours to execute? I would ...
2
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1answer
26 views

How can I get an algorithm to have an evalutation metric based on aggregate predictions?

Let's say I have a model that makes a prediction per individual. An example data set is below. Normally, evaluation metrics (for example within the XGBoost algorthim), are used at the individual ...
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0answers
66 views

How does the feval parameter influences the XGBoost training process?

In the package XGBoost, is possible to modify the feval (evaluated function) to a personalized one (as shown in the link: MAPE eval metric). I would like to know how is the training process of the ...
2
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1answer
303 views

Xgboost multiple class predictive performance beats one versus rest

I have an NLP task I'm tackling with xgboost (R implementation). Before describing my doubt I'll give you some background: I have a corpus of documents for which I did topic discovery, using a term ...
2
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1answer
174 views

Marketing Mix Model (MMM) using XGBoost?

Does anyone know of any literature on Marketing Mix Modelling (MMM) using XGBoost? Is this a viable techniques for MMM modelling? What would the advantages/disadvantages be? I think it would be a ...
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3answers
93 views

How to interpret feature importance (XGBoost) in this case?

I found two dominant features from plot_importance. My dependent variable Y is customer retention (whether or not the customer will retain, 1=yes, 0=no). My ...
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2answers
39 views

Determining threshold in an area with very few samples of positive label

I have a binary classification task where I want to either keep or discard samples. I have about a million samples, and about 1% should be kept. I want to discard as much as possible, but discarding ...
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1answer
17 views

Product Prediction to group of customers

I have multiple groups of customer, say for segment 1 as shown in the pictures, I have a list of products that I can choose the cross-sell to that group. Consider ...
1
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2answers
309 views

Xgboost in R: Dealing with extreme class imbalance

My data has an extreme class imbalance - 99.7% is 0's and 0.2% is 1's and almost all the predictor variables (6 out of 7) are categorical. I trained an xgboost classifier after performing an ...
6
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3answers
761 views

XGBoost Huge Dataset ~1TB

Can a gradient boosting solution like XGBoost or Lightbgm be used for a huge amount of data ? I have a csv file of 820GB containing 1 Billion observations and each observation has 650 datapoints. Is ...
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1answer
131 views

XGBoost: Can the features in test data be a subset of the features used to train the model?

Is it a problem if the test data only has a subset of the features that are used to train the xgboost model? All my predictor variables (except 1) are factors, so one hot encoding is done before ...
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1answer
43 views

How to interpret a random variable in the variable importance?

I have a problem, for simplicity let's say it is a binary classification problem. I am trying to solve this problem using XGBoost. A standard output plot for any ML algorithm, is the feature ...
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1answer
46 views

correct ML approach

I wanted to get your thoughts on a problem I have been facing. I have daily level product sales information (about 4 years). The sales are affected by the typical factors such as seasonality, day of ...
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0answers
115 views

XGBoost not learning

I have developed a train set for XGBoost to apply a learning to rank function on top of with the following parameters: ...
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1answer
1k views

Is the number of iterations in gradient tree boosting just the number of trees?

I have been searching for a while and I just can't find any indication. When people talk about iterations in algorithms like XGBoost or LightGBM, or Catboost, do they mean how many decision trees i.e. ...
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1answer
66 views

GBM: small change in the trainset causes radical change in predictions

I have build a model using transactions data trying to predict the value of future transactions. The main algorithm is Gradient Boosting Machine. The overall accuracy on the testset is fine and there ...
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3answers
84 views

Can this problem be solved using deep learning?

I want to predict price of used cars. I have data like this: Is this problem suitable for deeplearning or Should I use XGBOOST, RandomForest etc.? I used one hot approach for nominal features and ...
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1answer
2k views

What is the outcome of a Cox regression in xgboost?

I am creating a model using xgboost. Regarding its parameters, its objective is survival:cox ...
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1answer
84 views

xgboostclassifier prediction error after saving the model and restoring it

I have trained a xgboost model and during training, the prediction works fine. But if I stop the script and start a restoring script to restore and predict, then for the same test dataset I get every ...
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0answers
59 views

Variable Importance changes with oversampling

I am currently using Xgboost for a binary classification problem with highly imbalanced data in R. I have used oversampling to train the model. This worked well, now however it comes to measuring ...
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0answers
785 views

Bayesian optimization for a Light GBM Model

I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choice) ...
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0answers
2k views

Custom loss function for XGBoost [duplicate]

I am looking for code to implement a custom loss function instead of just classification error, or cross entropy for gradient boosted classification trees. We are trying to model regime detection in ...
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1answer
132 views

XGBOOST CV() producing error

I am getting the following error while using xgboost.cv() (scikit-learn interface). I am working on a regression problem. Below is the code and trace. No idea why ...
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1answer
59 views

Training a model where each response in the observation data has a different known varience

I have a dataset where each response variable is the number of successes of N Bernoulli trials with N and p (the probability of success) being different for each observation. The goal is to train a ...
2
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2answers
1k views

Why does it not need to set test group when using 'rank:pairwise' in xgboost?

I'm new for learning-to-rank. I'm trying to learn the Learning to rank example provided by xgboost. I found that the core code is as follows in rank.py. ...
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0answers
42 views

Suggestion for model performance improvement for ML competition

I am working on highly imbalanced dataset and trying to increase accuracy(metric: roc_auc) of my model which is hovering around 82-83%. This is part of an internal ...
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2answers
1k views

Random forest vs. XGBoost vs. MLP Regressor for estimating claims costs

Context I'm building a (toy) machine learning model estimate the cost of an insurance claim (injury related). Aim is to teach myself machine learning by doing. I have settled on three algorithms to ...
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2answers
1k views

Hyper parameters tuning XGBClassifier

I am working on a highly imbalanced dataset for a competition. The training data shape is : (166573, 14) ...
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1answer
6k views

Lightgbm vs xgboost vs catboost

I've seen that in Kaggle competitions people are using lightgbms where they used to use xgboost. My question is: when would you rather use xgboost instead of lightgbm? What about catboost?
3
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0answers
293 views

Adjust class weights due to class imbalance and class importance Multi class classification XGBoost

With respect to this question and the answer given by @Esmailian, Would anyone be able to let me know if Class B has a higher importance or the positive class ( i.e. it needs to have a higher ...
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0answers
42 views

if new feature downgrade the score for xgboost what do I have to look at?

let say I'm predicting the housing price of Boston(kaggle). if I got some score x then I added new feature y_K if this new feature drop the score. what is wrong with this feature and what do I ...
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1answer
46 views

xgboost and linear regression new feature analysis

For linear regression, seems like a new feature has to be a linear relation with the target variable. But If you make the new feature for the Xgboost, what do you have to consider to make a new ...
3
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1answer
1k views

XGBRegressor hyperparameter optimization using xgb cv function

I am trying to optimize hyper parameters of XGBRegressor using xgb's cv function and bayesian optimization (using hyperopt package). Here is the piece of code I am using for the cv part. ...
1
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1answer
399 views

correct setting of eval_set in multiclass classification xgboost python , error is “ Check failed: preds.size() == info.labels_.size()”

i have a multiclass classification problem with 3 classes [-1,0,1] . i'd like to use eval_set in xgboost. but it fails with error: ...
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0answers
61 views

Training xgboost model with more data having different characteristic

I have trained my model for ECG data which has 8528 ECG files having length 30s and sample rate 300 so total file length in csv ...
1
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1answer
388 views

xgboost GridSearchCV take too long or does not goes to the next step

just strange ...
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2answers
38 views

Given a single discrete data set, how should I divide it into training data and test data?

I have a dataset in libSVM format consisting of 6000 entries, each with 5 indices, and each index has a binary value 1 or 2. Each of the 6000 entries has a label of ...
2
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1answer
1k views

XGBOOST (sklearn interface) REGRESSION error

I am trying to run a GRIDSEARCHCV (sklearn) on XGBRegressor. Documentation on the parameter says that if regression, then objective = reg:squarederror.(see https://...
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0answers
86 views

Why am I getting accuracy of Xgboost model 0.00%?

I am trying to build Job Recommender System using Deep Learning. dataset used From this dataset i have taken only users.tsv, user_history.tsv, apps.tsv and jobs.tsv to build a hybrid recommender ...
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2answers
351 views

Discrete Ordinal Classification with Probabilities

If I have classes 1, 2, 3 and 4. But, I also need the probability for each of the other classes. I'm currently using XGBoost for one-vs-rest classification, but that means we're losing information ...
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0answers
89 views

Reverse engineering on Xgboost model

I am doing experiments on https://www.physionet.org/challenge/2017/sources/ submission. I like one of the submission code, which use Xgboost to train the ...
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1answer
2k views
2
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1answer
94 views

How do I train Xgboost classifier for ECG Signal data?

I am testing https://www.physionet.org/challenge/2017/sources/ submission. I like one of the submission code, which use Xgboost to train the classifier. Training ...

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