Questions tagged [xgboost]

For questions related to the eXtreme Gradient Boosting algorithm.

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12 views

Consequences of using XGBoost regressor for small dataset(< 500 rows)

I am using XGBoost regressor to train my model for 322 rows of data and the train and test split is as follows: ((257, 9), (257,), (65, 9), (65,)) I am using the ...
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Re-training regression model on covid data

I am trying to re-train a regression model (XGB regressor) which was used in the pre-covid times (Feb 2020). The dependent variable for the model is the number of bookings done, and due to covid, the ...
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F1 score /Sorensen Dice / Bray Curtis Distance/ XGBoost / Custom objective function [closed]

My question is: How can I use the Bray Curtis distance as a custom objective function for XG Boost in R? Bray Curtis is a proxy for F1 score. For two vectors, Labels and Preds, the BC distance is Sum ...
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Is addition of a random control variable a good idea, to ensure that results are correct?

I'm dealing with a complex dataset with many patients who have a condition, and various qualities about these patients. I'm trying to determine patient outcome based on patient qualities. I'm using <...
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XGBoost with deep trees

I've been exploring the use of XGBoost in many different applications. Up to now, I always find the best results with shallow trees (from 1 to 3 levels), with the rest of the parameters very dependent ...
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16 views

Cost Function Binary Classification

I have imbalance dataset for binary classification problem. I want to create a custom cost function that takes into account not only the actual class and probability, but another variable "...
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1answer
18 views

Dealing with unseen data/categories in machine learning models for stream data

I want to build a machine learning model (xgb and lgbm) that has to handle streaming data on a weekly basis. The models are trained on a bi-weekly basis. The data includes order information and I want ...
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How are missing values treated in XGB RF Classifier?

I was exploring Random Forest Classifier in XGBoost listed here : https://xgboost.readthedocs.io/en/latest/python/python_api.html I was wondering how the missing values will be handled in this ...
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23 views

100% Accuracy on test dataset using a previous developed model oputput

My dependent variable is a probability that is sourced from someone else's classification model. I am using this probability as a dependent variable as I don't have the actual data. On building an ...
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How to incrementally train xgb with additional features?

I found this thread similar to my question: How to reach continue training in xgboost However, I'm looking for a way to incrementally train my xgb model with ...
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How to explain ANN can predict much larger output values (e.g., y>2.5) when it was only trained with small output values (y>=2.5)

I have trained models with both ANN and XGBoost. I am wondering that whether ANN has the ability to predict much larger output values (e.g., $y>2.5)$ when it was only trained with small output ...
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Transforming binary data for decision trees

I have binary columns in my dataset (20) e.g. hot_weather, discount (y or no), where in each case 1 = yes no = 0. I am using this data on tree based methods. It is a regression problem and my RMSE is ...
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What data science models are best for explainability for regression problems?

Imagine you have to create a model to explain to stakeholders e.g. to predict price, weight, sales etc.. Which machine learning models offer the best in terms of explainability and interprability? ...
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Time Series Modelling or Simple regression or something else

PROJECT: I am working on an e-commerce site where digital products can run out so there is need to reorder them 72h before they run out (reordering them sooner is not a problem but having notification ...
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1answer
17 views

How does tree-based algorithms handle linearly combined features?

While I am aware that tree-based algorithms (e.g., DT, RF, XGBoost) are 'immune' to multi-collinearity, how do they handle linearly combined features? For example, is there is any additional value or ...
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Timeseries param tuning using XGBOOST

I am using xgboost for timeseries forecasting of a certain attribute while including seasonal features.Trained on nearly 4 years of data and tested on the last month. My rmse is as below : Hyper ...
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2answers
29 views

Using Transaction Amount to Guide Learning in an Fraud Detection Machine Learning Model

I am currently using transaction amount as a feature in an XGBoost classification model designed to identify fraudulent transactions. Furthermore, transaction amount is bounded for this problem ...
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kmeans cluster results for use as xgboost feature

I am curious if kmeans clusters can be used as xgboost features, along with the original features? Specifically for features X and labels Y can we: Split into X_train, y_train & X_test, y_test ...
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XGBoost: Typical gamma and min_child_weight range

What is the typical accepted range of gamma and min_child_weight parameters for the XGBoost algorithm? Is the range of min_child_weight correlated with the number of feature or samples in the training ...
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1answer
30 views

Strong overfitting accompanying strong class imbalance

I'm training an xgboost binary classification model. The data I have is around 600k and positive is only 0.1% of it. I tried to use all overfitting prevention techniques xgboost has to offer (tune eta,...
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Confusion Matrix after XGBoost is showing positive as negative class

Please can you help me with confusion matrix. I've implemented the XGBoostClassifier. After fitting the model when I looked to the confusion matrix to view the performance on Test data. The confusion ...
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68 views

Xgboost : A variable specific Feature importance

I have a data set something like this: ...
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1answer
35 views

Feature importance difference in two similar machine learning models

Situation 1: I have trained a text classification model (Model 1) which gives me a probability of true class as X. I have also trained a classification model (Model 2) using only the categorical and ...
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XGBoost Regressor model reproducibility

I am running an XGBoost Regressor to predict electricity consumption (load) and further classify predicted values as peaks or not. As for dataset I started with hourly energy load data + hourly ...
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1answer
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Output of evaluation metric for XGBoost - is it cumulative?

On the 10th boosting round for XGBoost, I get an MAP of 0.32 on the test data. Does that reflect the performance of just that 10th tree? Or the performance of all 10 trees combined that have been ...
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Is there a python package that includes decision tree structures that can be used with a genetic algorithm?

I'd like to use decision tree / forest but I need to use a special objective function that can't be differentiated and hence I can't use XGBOost etc. That leaves a genetic algorithm where I use the ...
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Is there a good alternative to XGBoost for learn to rank?

My problem with XGBoost is that when I load the train dataset into the XGBoost DMatrix, there is a memory spike that is unavoidable, and I can't get my dataset loaded into RAM without crashing first. ...
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XGBoost feature importance has all features but decision tree doesn't

I have used XGBoost to train a model with 400 features. My understanding is that since the max_depth is default at only 6, and 2^6 < 400, not all features will end up in the tree. How come when I ...
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1answer
22 views

Does Gradient Boosting perform n-ary splits where n > 2?

I wonder whether algorithms such as GBM, XGBoost, CatBoost, and LightGBM perform more than two splits at a node in the decision trees? Can a node be split into 3 or more branches instead of merely ...
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sklearn GradientBoostingClassifier provides unstable predictions

I am using sklearn.ensemble.GradientBoostingClassifier to build a rather simple model which predicts probabilities. I have simplified the problem to having only one numerical predictor and the outcome ...
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Prediction problem across a wide space

I have assembled a database of Clash Royale games in an attempt to understand the outcomes of various match-ups. The game is composed of an 8 card deck drawn from 102 cards. As you can see from the ...
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how to represent feature importance in xgboost in percentage?

I am looking for a way to represent the feature importance numbers in percentage. I read through articles and API documentation for XGboost in python and it gives me the feature importance score, ...
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How should I add reference key column to output after modeling is Complete?

0 I have created a csv of data with the following columns: (1) app_key (2) churn, (3)tenure https://i.stack.imgur.com/NAlFF.png I have performed the following code in order to drop app_key and churn <...
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How can I use a validation set to tune the hyperparameters of an XGBClassifier?

I'm currently building a ranking model using an XGBClassifier. I have training, testing, and validation sets. I want to use the validation set to tune the hyperparameters of the XGBClassifier before ...
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1answer
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Why might my validation loss flatten out while my training loss continues to decrease?

In my effort to learn a bit more about data science I scraped some labeled data from the web and am trying to classify examples into one of three classes. I am running into a problem that, regardless ...
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1answer
118 views

From logistic regression to XGBoost - selecting features to run the model with

I have been asked to look at XGBoost (as implemented in R, and with a maximum of around 50 features) as an alternative to an already existing but not developed by me logistic regression model created ...
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2answers
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Probabilities predicted by XGBoost

I looking at football data and trying to predict whether a goal will occur using xgboost with objective binary: logistic. My data is 1:10 unbalanced with no goals being more dominant. I have used ...
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1answer
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how to prevent machine crash while searching for hyper parameters of XGBoost with GridSearchCV

I am searching for best hyper parameters of XGBRegressor using GridSearchCV. Here is the code: ...
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1answer
71 views

Negative R2_score Bad predictions for my Sales prediction problem using LightGBM

My project involves trying to predict the sales quantity for a specific item across a whole year. I've used the LightGBM package for making the predictions. The params I've set for it are as follows: <...
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1answer
117 views

How to approach All vs All classification problem

Let's say you are building a Star trek style medical tricorder which can diagnose any medical condition. It needs to be able to detect comorbidities where a patient has multiple conditions (e.g. ...
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29 views

how do tree based methods deal with missing feature columns?

all, i have trained a model using xgboost. Some of the features are one hot encoded e.g. currency where it is either gbp or usd. it seems that when i output the feature importance gbp and usd were in ...
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30 views

Feature Selection before modeling with Boosting Trees

I have read in some papers that the subset of features chosen for a boosting tree algorithm will make a big difference on the performanceso I've been trying RFE, Boruta, Clustering variables, ...
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2answers
55 views

Hyperparameter tuning XGBoost

I'm trying to tune hyperparameters with bayesian optimization. It is a regression problem with the objective function: objective = 'reg:squaredlogerror' $\frac{1}{2}[log(pred+1)-log(true+1)]^2$ My ...
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1answer
29 views

Bias variance tradeoff boosting (xgboost) vs random forest (randomized bagging) which to use when?

I was looking up differences between boosting an bagging and I see this quoted everywhere If the classifier is unstable (high variance), then we should apply Bagging. If the classifier is stable and ...
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1answer
37 views

Why Continous Variable Buckets Overfitting model

I have a continuous (high cardinal discrete) variable 'numInteractionPoints' in my dataset during training model - I binned this feature in order to avoid overffing , first top bar chart is from ...
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76 views

CNN with XGBoost as an output layer, is it better?

Context: I have been experiencing with some Kaggle datasets to learn more about image classification. So, in this binary image classification task, I tried something that I thought would increase the ...
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1answer
19 views

How to quantify ‘compute cost’ of training of xgboost model?

I want to quantify compute cost of hyper-parameter search for xgboost model. One way can be to measure training time with one particular hyper parameter configuration chosen for training and use it as ...
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1answer
28 views

Tree-based algorithms and ordinal features

For tree-based methods (e.g., DT, Random Forest, Gradient boosting, etc.), does the conversion interval of an ordinal feature to continuous matter matters? (I can see why it matters for linear model, ...
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1answer
55 views

Python sklearn model.predict() gives me different results depending on the amount of data [closed]

I train my XGBoostClassifier(). If my testing set has: 0: 100 1: 884 It attempts to predict 210 1's. Around 147 are wrong (False positives) and 63 1's correctly ...
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22 views

How much does each tree of gradient boosting contribute to the global feature importance?

Let's say we are training a GBDT in the Titanic dataset. We have 3 trees in the GBDT. You extract the first tree and calculate the feature importance (no matter if cover, gain...), and Age importance =...

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