Questions tagged [xgboost]

For questions related to the eXtreme Gradient Boosting algorithm.

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1answer
24 views

XGBoost non-linear regression

Is it possible to use XGBoost regressor to do non-linear regressions? I know of the objectives linear and logistic. The ...
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1answer
16 views

Reasons for a model predicting probability of being class 1 at x value

All, This is a general question. I have a binary classification which predicts if someone is rich or not. I had a question from someone asking that if the probability someone is rich is 0.6 and ...
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0answers
23 views

Is there a Softmax-like transformation with scale-invariance and linarity?

At the moment I'm using XGBoost to generate a prediction of probabilities with a custom objective-function to build something like an expert system. To do so I need to transform the raw XGBoost ...
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1answer
26 views

Should I add string feature columns?

If my dataframe looks like this: ...
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0answers
11 views

What are the tradeoffs associated with the various XGBoost interfaces?

I'm spinning up with XGBoost today, and have already encountered at least two ways to train gradient boosted trees: Approach 1 ("native" xgb) source: XGB python intro ...
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1answer
26 views

Feature engineering using XGBoost regressor [duplicate]

If I want to train a regression model through tree based algorithms like XGBoost. Suppose that there have 5 features x1, x2, x3, x4, x5 and a target y. And some experts said x2 minus x3 is highly ...
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1answer
22 views

New classification in Machine Learning model with xgboost

I write a code in Rstudio with xgboost to solve a Machine Learning problem. This is my actual code: ...
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1answer
26 views

How to make predictions on unseen data with different cardinality using xgboost

I am training an XGBoost regression model on a feature set $X$ that includes a feature $x_k$ with high cardinality (~100). First, I am using one-hot-encoding to convert $x_k$ and then split the set ...
2
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2answers
146 views

Influence of imbalanced feature on prediction

I want to use XGB regression. the dataframe is coneptually similar to this table: ...
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1answer
24 views

Error in xgboost

This is my script: ...
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0answers
8 views

Imbalanced Classification: BOW vs doc2Vec in XGBoost with sample weights

I am new to machine learning. I have an imbalanced dataset of pages of reports with class 1: 97%, class 2: 2.2% class 3: 0.25% which are the different type of pages I am mostly concerned with ...
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1answer
43 views

XGboost predict

I am trying to understand this XGboost example. After training: ptrain = bst.predict(dtrain, output_margin=True) they make prediction on test data, but the problem ...
1
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1answer
19 views

Sklearn xgb.fit: TypeError: fit() missing 1 required positional argument: 'y'

I am new to ML, and XGB is really confusing me. I understand that for Python XGB can be imported directly from the xgb library or via SKLearn. The methods for xgb from the direct xgb library also ...
3
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1answer
29 views

classification balanced target y [0,1] but imbalanced feature x [many 0 , few 1s] , maximize precision

I have a simple dataset with balanced target y (0 or 1) ,and imbalanced feature (many 0 , few 1's) I aim to get high precision (don't care about recall) I can get high precision of 0.53 if I just ...
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0answers
33 views

XGBoost in Python: How do I input the scale_pos_weight for multi-classes?

I have 3 classes and my weights are Class 1: 1 Class 2: 0.333 Class 3: 0.167 How do I input them into the scale_pos_weight parameter? I know for a binary classification, we input it like below: ...
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1answer
40 views

ColumnTransformer worse performance than sklearn pipeline

I have an (unbalanced , binary data) pipeline model consisting of two pipelines (preprocessing and the actual model). Now I wanted to include SimpleImputer into my ...
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0answers
47 views

How does XGBoost perform in Parallel

So what I know about boosting technique, Like we train the data and update the weights of falsely predicted values or try to minimize the loss in the next model. So basically, it's the sequential ...
1
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2answers
159 views

Python xgboost predicting future events

This is related to this article: https://towardsdatascience.com/forecasting-of-periodic-events-with-ml-5081db493c46 I found it interesting and tried to replicate it, having as a result a xgboost ...
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0answers
20 views

Different AUC values for xgb and sklearn built in functions [duplicate]

The model is trained with early stopping on a validation set: ...
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0answers
10 views

XGB Regression: Is there a way to handle somewhat bimodal Y variable?

I am using XGBRegression to predict on continuous percentage data with 80% of the values around 100, 10% around 0 and 10% data distributed in the middle. Models are struggling with predictions around ...
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0answers
60 views

Can XGBoost support vector outputs?

I am interested in fitting data (regression rather than classification) with individual targets which are vectors via an XGBoost type model. However, currently Python's xgboost.XGBRegressor model only ...
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0answers
17 views

XGBoost for a binary classification where features are of different types

I have a dataset of "questions in an exam" that contains features such as: QuestionLength (float) averageTimePerQuestion (float) hasMedia (boolean, represented as 0 or 1) averageOfHardWords ...
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0answers
24 views

Gradient boosting algorithms and filling categorical variables

I have house prices dataset Link on Kaggle and I am having some dilemma. Some categorical variables having explicit majority. If we look at MSZoning and SaleType columns, there is "RL" type ...
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0answers
12 views

For a Multi-class Classification Problem, What are the Pros and Cons of using a Cascade ML model versus Single Multi-class Classification model?

I am developing an ML model for classification using tabular data. It has 5 classes right now and new classes are expected to be continuously added. (Already have a new one leading to an imbalanced ...
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0answers
15 views

What is the best way to create input data samples using in XGBoost for predicting number of next days that customer will come back to store

I'm building the tree-based model like a XGBoost to solve the problem about customer purchase cycle. And I think, I will build 2 models which one is predicting the customer will come back to store in ...
2
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2answers
34 views

confused on "real score" vs "decision value" in classification trees

I'm reading the guide to XGBoost and am confused about the distinction it draws between the scoring systems of decision trees and classification/regression trees. The paragraph I am hung up on is: A ...
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0answers
16 views

Classification for Ordinal labels - what tree-based methds can i use?

I have a label that has a natural ordering e.g. 0,1,2,3 where 0 is the worst activity measure and 3 is the best. For each label given by the model i need to also give the probability that it belongs ...
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0answers
16 views

lack of consistency in Bayesian optimization of xgboost's hyperparameters

I am trying to optimize the hyperparameters in an xgboost model using Bayesian optimization and the mlrmbo R package. The simplified code below seem to produce reasonable results, but the problem I ...
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0answers
16 views

User Churn Rate analysis - Binary classification

I have a dataset which has the logs of user clicks. This is a trail version(2 months) of the software. Users can use a special feature during this trail period to improve their sales. The number of ...
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0answers
9 views

Train and test data fixed during boosting?

I have question about boosting algorithm. I know that boosting is a sequential process and it gives high weight to misclassification of previous model. Then, its' train and test data are fixed through ...
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0answers
13 views

Why is the average prediction moving away from average response for a reg:gamma model

I'm predicting a response that I would typically model under a gamma distribution, with relatively simple paramters, I'm just using the default other than these: learning_rate = 0.01 max_depth = 6 ...
2
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1answer
41 views

Labeling and aggregating features issue

I am trying build a simple binary classifier (some tree based algorithm for now) and my training data will have features aggregated at the user level. So I'll have a unique records of each user. These ...
2
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2answers
154 views

Flask output not showing

I am trying to deploy a XGBClassifier model using flask. After giving the values to the relevant fields on the webpage, the ...
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0answers
18 views

Feature importance not providing score for each feature

I have a data frame of this shape (808616, 10744) and I use this model ...
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0answers
56 views

Should I use or tune `reg_lambda` or `reg_alpha` hyperparameters when using a tree booster in XGBoost

XGBoost has 3 types of boosters: tree boosters (gbtree, dart) linear booster (gbliner) ...
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1answer
39 views

How to interpret .get_booster().get_score(importance_type='weight') for XGBRegressor()

I am trying to do feature selection using XGRegressor(). I am doing this because I have many features to choose from over 4,000. Once I have a set of features I have a neural network I created to use ...
2
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1answer
54 views

Possible to use predict_proba without normalizing to 1?

I'm using xgboost multi-class classifier to predict a collection of things likely to fail. I want to run that prediction, and report anything that the classifier ...
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2answers
56 views

aggregation of feature importance

I have more of a conceptual question I was hoping to get some feedback on. I am trying to run a boosted regression ML model to identify a subset of important predictors for some clinical condition. ...
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1answer
150 views

Is feature importance in XGBoost or in any other tree based method reliable?

This question is quite long, if you know how feature importance to tree based methods works i suggest you to skip to text below the image. Feature importance (FI) in tree based methods is given by ...
-1
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1answer
101 views

nested cross validation vs. train-test split

I am trying to understand the main benefits of conducting a nested cross-validation compared to a simpler train-test split. Let us say I would like to build a prediction model. I initially split my ...
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2answers
22 views

Concerns regarding small dataset with too many features

I have dataframe with 322 observations with 224 features. The observations has two classes, 0 or 1,which i'm trying to predict. class 0 has 168 observations and class 1 has 154 observations. I was ...
0
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1answer
18 views

Gamma parameter in xgboost

As per the original paper on xgboost, the best split at a node is found by maximising the quantity below $ \cal{L}_{\rm split} = \frac{1}{2} \sum \left [ \frac{G_L}{H_L + \lambda} + \frac{G_R}{H_R + \...
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0answers
10 views

Why is my validation score so much higher using TargetEncoder?

So I'm experimenting a bit with an XGBoost model & encoding the categorical variables using the target encoder from the category_encoders library. The code below shows how I split the dataset and ...
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0answers
17 views

How Random Forest and XGB 'Regressor' calculate feature importance

I am searching about how Random Forest and XGB 'regressor' calculate feature importance. However, the most of discussion focus on Classifier. I try to find out the answer in the official document but ...
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0answers
21 views

Is XGBoost active in the Gradient Boosting widget on the Orange platform?

When I open the Gradient Boosting widget xgboost is grayed out even though it is described in detail under the documentation section on Orange. The two options are Gradient Boosting (scikit-learn) and ...
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0answers
12 views

xgboost performance

XGBoostRegressor is not performing better than AdaBoostRegressor for the same set of parameters for some reason. Since my ...
2
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1answer
28 views

My own model trained on the full data is better than the best_estimator I get from GridSearchCV with refit=True?

I am using an XGBoost model to classify some data. I have cv splits (train, val) and a separate test set that I never use until the end. I have used GridSearchCV to determine the best parameters and ...
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2answers
33 views

XgBoost given targets its only feature but fails when test targets are outside the range of training targets?

I'm learning to use XgBoost, and I'm doing an exercise involving predicting prices. However I'm noticing some weird behavior where XgBoost's predictions deviate from the target value even if I'm ...
2
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0answers
27 views

Static ML model or Time-Series? How to model/predict a binary target when I have time variant features but most features are constant?

I have been working with Real World data from patients. I have a dataset with information about 10million patients; Collected over a span of varying duration (5 to 20 years). What I am predicting is ...
2
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1answer
22 views

What if root of a such tree is pruned in xgboost?

Extreme Gradient Boosting stops to grow a tree if $\gamma$ is greater than impurity reduction given as eq (7) (see below) , what does happen if tree's root has a negative impurity? I think there is no ...

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