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Questions tagged [boosting]

Boosting is a machine learning ensemble meta-algorithm in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Also: Boosting is the process of enhancing the relevancy of a document or field

From [the docs]:

"Boosting" is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.

Also:

From the docs:

Boosting is the process of enhancing the relevancy of a document or field. Field level mapping allows to define an explicit boost level on a specific field. The boost field mapping (applied on the root object) allows to define a boost field mapping where its content will control the boost level of the document.

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Classification report outputs 1 in every column

X=df.drop('class', axis=1) y=df['class'] X=pd.get_dummies(X, drop_first='True') from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=101) from…
Aaqib Khan
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Multi-class Fairness Metric Implementation

FairGBM is a fairness-aware ML model built upon LightGBM. You pass in your training data matrix (X) and training labels vector (Y), like you would normally do any gradient boosting algorithm and fit the model. However, if you want the model to be…
User_13
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Visualize trees in LightGBM

I want to plot trees with their splits, which plays in LightGBM model. In DecisionTree I can use Graphviz for that. How can I visualize any tree from boosting ensemble?
Любовь Пономарева
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Boosting vs. Ensemble models

Can someone compare and contrast these two concepts in layman terms for me? The definitions sound similar but I know there has to be more differences between the two. I have: Ensemble models: Combines multiple ML models together to get a…
Katsu
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Is there any way to include time-varying variables in CoxBoost or GLMBoost?

I am currently struggling with the following problem in R: I would like to use a Cox model with time-varying variables to perform my variable selection via boosting. iCoxBoost takes a Surv object as formula which allows start-stop notation. But when…
reginameuser
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Trying to use tidymodels for a catboost model: Receiving error related to labels

Here is the model: cb_spec <- boost_tree( mode = "classification", trees = 1000, tree_depth = tune(), min_n = tune(), mtry = tune(), learn_rate = tune() ) %>% set_engine("catboost", loss_function = "Logloss", task_type = "GPU") Here…
Robert Tedesco
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How to increase the weight of observation in boosting regression

How to increase the weight of observation in boosting regression, like for boosting classification, you could add more weights to the observations who predict wrong but for boosting regression, how could you know which observation is wrong and add…
foyeyefo
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Is there a way to visualize GradientBoosting or XGboost after modeling

Is there a way to visualize GradientBoosting or XGboost after modeling in python? Like any plots.
foyeyefo
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Graph for Train and Validation set (overfitting?)

I'm using xgb and have hypertuned my parameters using hyperopt, however when I plot the the train set and validation set after fitting my model, I noticed that the lines intersect with each other, what does that mean? Also the validation line…
Hojiyama
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Which metric to use for imbalanced classification problem?

I am working on a classification problem with very imbalanced classes. I have 3 classes in my dataset : class 0,1 and 2. Class 0 is 11% of the training set, class 1 is 13% and class 2 is 75%. I used and random forest classifier and got 76% accuracy.…
Anthony G
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Converting H2O AutoML Model to Sklearn Model

I have an H2O AutoML generated GBM model using python. I wonder if we can convert this into a standard sklearn model so that I can fit it into my ecosystem of other sklearn models. I can see the model properties as below when I print the model. If…
Sreenath1986
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How to combine two different prediction models in R

I recently was reading about ensemble models, and I want to try constructing one with my data. I want to randomly combine my two models with the lowest RMSLE scores into one similarly constructed model. My first model uses a boosted regression tree,…
Jamie Warren
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How does LightGBM calculate the leaf values for the first tree in regression?

When plotting the first tree from a regression using create_tree_digraph, the leaf values make no sense to me. For example: from sklearn.datasets import load_boston X, y = load_boston(return_X_y=True) import lightgbm as lgb data = lgb.Dataset(X,…
M. Berk
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How to develop self-learning gradient boosting classifier

I have trained Gradient Boosting classifier, but when I validated the model on completely new data, the resuls were, due to totally different data, poor. I have sample data from production process and my supervisor says it is normal that the errors…
ZuzanaTelefony
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How does LightGBM convert feature_fraction to an integer value?

Does anyone know how does lightgbm convert feature_fraction parameter, which is defined by user as non-integer like 0.8, to integer values? Does it use floor or ceiling function? I could not find it in the documentation. (and a skim over the source…
Alireza Amani
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