Questions tagged [xgboost]

For questions related to the eXtreme Gradient Boosting algorithm.

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22 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
25 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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22 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
17 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 ...
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1answer
19 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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2answers
26 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 ...
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1answer
16 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
14 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 ...
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1answer
11 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 ...
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1answer
23 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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18 views

XGBClassifier Feature Order

I'm working with XGBClassifier from the XGBoost Python API and recently found out that the class requires feature columns to be in the same order for predictions as they were used for training. For ...
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3answers
31 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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1answer
24 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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22 views

Using bagging and random forests together

I was looking at a kernel implementation (for text classification) and the following piece of code got me a little bit confused (I removed part of the features - in order to keep it light - as most of ...
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1answer
8 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 ...
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1answer
33 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 ...
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69 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
22 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
14 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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0answers
28 views

Bad input shape; XGB

I'm trying out a simple code to test the xgboost library in python. My input matrix has 17 features and 16,718 observations X = (16718,17) My output has 3 ...
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1answer
37 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
35 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
38 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
42 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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2answers
49 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
260 views

What is out come cox regression in xgboost

I am running xgboost where objective is survival:cox and eval_metric is cox-nloglik. Y range from -800 to 800. However, predicted values are way to large in range from 10^3 to 10^13. I am not sure why ...
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1answer
13 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
48 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
65 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
213 views

Custom loss function for XGBoost

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
28 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
49 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 ...
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2answers
101 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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28 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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1answer
212 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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1answer
75 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
727 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?
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185 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
15 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
25 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 ...
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1answer
269 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. ...
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1answer
32 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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26 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 ...
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1answer
63 views
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0answers
38 views

XGBoost multiclass class balancing using weight parameter [duplicate]

I have three classes in the target variable with representation ratios of, class A:0.5 class B:0.3 ...
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2answers
19 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 ...
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233 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
42 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
108 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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33 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 ...