Gradient Boosting Machine

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Using gbm in R with non-random missing data

The way gbm handles missing variables in R is by using surrogate splits. Is this appropriate to use when the data is not missing at random?
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15 views

GBM Prediction Interval Issue

I need to get prediction interval for GBM model (loss='ls'). I'm using this example as a basis http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html My model ...
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1answer
44 views

Training AUC and CV AUC in Boosted Regression Tree

My question is regarding the differences in the training data AUC score and the cross validation AUC score in boosted regression trees (BRT) built using the gbm.step function in the dismo package. I ...
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1answer
22 views

Different minimum observations per node in GBM model does not affect the AUC. How to explain?

My dataset is 6.3 million observations, with 150 features for each one. 25 000 of these observations are positive case and the rest is negative case, so about 1:250 class balance. I've been training ...
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68 views

Using an RMSE with derived confidence interval, to generate a prediction interval for an estimate

Previous questions have asked about creating prediction intervals for estimates derived from random forests or boosted regression trees, in a similar way to is easily achieved with linear regression ...
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71 views

Combining collinear continuous predictor variables in GBM

I'm dealing with a dataset (n=254) with one dependent variable (Y), and three independent variables (X1, X2, X3), all continuous. I would like to compare the contribution from each IV to Y. I've been ...
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56 views

How can I compare GBM feature importances to GBM partial dependence plots?

I am having trouble reconciling the difference between the indicated "importance" from a GBM that I am calculating with what is shown in the partial dependence plots. I would expect higher ...
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87 views

R: selecting appropriate fit statistics from GBM output

I'm using the gbm.step function in the dismo package in R to evaluate the contribution of three continuous variables on my ...
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1answer
199 views

R: partial dependency plots from GBM package. Values and y-axis

I'm using the gbm.step package in R to look at the influence of three continuous variables on my continuous response variable. I have 234 observations. The model: ...
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60 views

Parameter selection for GBM

I'm building a Gradient Boosting model. Given a dataset and event rate, is it possible to get a formula/ definitive strategy for the optimum number of trees, shrinkage parameter and depth of trees? I ...
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1answer
256 views

Generating PMML export of a gbm model in R?

Is it possible to generate PMML of a gbm model? When I try to use the pmml library, I get an error: Error in UseMethod("pmml") : no applicable method for 'pmml' applied to an object of class ...
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3answers
269 views

Performance drop between training and validation datasets

I have been using R's GBM (Gradient Boosting Machine) package for several months. I typically split my data into three partitions: Training, Validation, and Testing. I use the validation data set to ...
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2answers
146 views

How to get coefficients of gradient booting models?

I tried gradient boosting models using both gbm in R and sklearn in Python. However, neither of them can provide the ...
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2answers
81 views

Estimating expected lifetime from hazard ratio and estimated base hazard function

Apologies if this is a basic question, I am not very familiar with survival analysis ... I have trained a gradient boosted Cox proportional hazards model in R, and have been able to obtain reasonable ...
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1answer
122 views

GBM Bootstrap Prediction Interval Code Error

based on code presented in thread: How to find a GBM Prediction Interval I am trying to apply this to my dataset. Below is my full code, and I am having issues with the bootstrap function. ...
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1answer
249 views

How to find a GBM Prediction Interval

I am working with GBM models using the caret package and looking to find a method to solve the prediction intervals for my predicted data. I have searched extensively but only come up with a few ...
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0answers
105 views

Is multicollinearity a problem with gradient boosted trees (i.e. GBM)?

A question about multicollinearity for random forests has been asked and answered, but what about boosted trees?
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62 views

Pitfalls of using random forest/GBM on proportion data?

I have a set of data with a dependent variable which represents a proportion, and many of the samples contain a response of 1. I would like to build a random forest or GBM regression model on the ...
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1answer
34 views

Can adaboost choose the same variable for multiple splits for a given tree?

Can adaboost choose the same variable for multiple splits for a given tree? The model was given 100 + variable to choose from and it did choose them for the other trees in the ensemble. I am using ...
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87 views

How does GBM model handle categorical variables with many levels

I am using gbm model to fit a continuous dependent variable Y with several categorical variables, say, X, Z, V, and W. Suppose X has many levels (distinct values) and Y has moderate number of levels, ...
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31 views

GBM package: Why there is a missing node?

Why there is a missingNode as 3 as there are no missing values? I have the data in the following form: ...
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0answers
185 views

How to interpret the output of a multinomial classification model in R package gbm

After running a gradient boosted model with n data points using multinomial regression where the response variable (a factor, as required by the gbm function) has ...
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0answers
16 views

Finding the effects of certain levels of a factor predictor

I have fitted a binary classification gbm model, and one of the predictor variables, Affiliate has 50 different levels. Given ...
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0answers
75 views

Sample Weights for classification using Gradient-Boosted trees?

How can "weights" be given to different samples according to their relative importance while using Gradient boosted decision trees for classification? How does the ...
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212 views

gbm R multinomial vs bernoulli

I am using the gbm package to fit a binary variable using several attributes, some numeric and some categorical. Since the output varible was defined as factor I initially did ...
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2answers
96 views

gbm.perf with method = “test” returns n.trees from last run

As the title says, I'm getting some interesting results from gbm.perf. The first time I ran into trouble was after a run where n.trees was set to 7,000. When gbm.perf also returned 7,000 I got ...
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46 views

Estimating the running time of gbm grid tuning

I am trying to estimate the time it will take me to run a tuning grid on gbm (I am using the R Caret package but this is irrelevant as I am interested in the relative processing time). I can see many ...
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75 views

BRT analysis using count data

I have some problems with my BRT analysis. Introduction to the data: The dependent variable is count data of a specific palm species in SA, and the predictors consists of nine various kinds of ...
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2answers
324 views

How to find optimal values for the tuning parameters in boosting trees ?

I realise that there are 3 tuning parameters in the boosting trees model, i.e. the number of trees (number of iterations) shrinkage parameter number of splits (size of each constituent trees) My ...
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82 views

How do i estimate the Weights of the predictions assigned to each of the tree in GBM using R? How does GBM split nodes?

I ran a GBM model in R with loss function as bernoulli and n.trees=1000. I want to see the weights assigned to the predictions coming from 1000 trees. Is there any command in R that does that? How ...
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1answer
140 views

Negative predictions for binomial predictions from gbm in R

I've just fit a binomial model (training y = 0 or 1) using R's gbm package. When I calculated predicted values using my validation data, some of the predicted values were less than 0. Is this normal ...
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2answers
173 views

Combine decision trees from GBM to reduce output

I am curious if any research has been conducted to efficiently combine trees resulting from a gradient boosting process. I routinely run a process that generates 20 or 30 thousand trees in R. I then ...
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73 views

R: Which distribution to use with gbm for gamma distributed data?

When I use GLMs I can use the option family="Gamma" for analysing data consisting of positive real numbers. Also package gbm ...
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4answers
1k views

Why doesn't Random Forest handle missing values in predictors?

What are theoretical reasons to not handle missing values? Gradient boosting machines, regression trees handle missing values. Why doesn't Random Forest do that?
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1answer
221 views

How can I modify default parameters of a gbm.step plot?

I am using the function gbm.step() from the dismo package to assess the optimal number of boosting trees using k-fold cross ...
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53 views

Random Forest and Factor Predictors [duplicate]

How do decision tree based ensembles like random forest deal with categorical ("factor") predictor variables? My guess would be that indicator variables are created for each factor via a ...
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93 views

modeling rates with machine learning tools (svm, gbm, nnet)

I have a numeric integer variable that is knowly proportional to an exposure measure plus other continuous / categorical covariates. If I were to use classical log-linear glms i would model ...
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200 views

Binary Class Distribution Effects on Probability Scores - (gbm) Boosted Tree Regression Models

Any help would be greatly appreciated. Problem: I need help to better understand the probability scores that come from the result of a decision tree model. Specifically, I'm using the gbm package ...
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0answers
181 views

Could you explain how gradient boosting algorithm works?

I have read a lot about gbm in Greedy function Approximation: A Gradient boosting Machine (pdf), but I can't code the algorithm for example LS_Boost in a simple way. Can someone explain what $h(x;a)$ ...
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2answers
214 views

Heuristic Feature Selection for Gradient Boosting

I originally posted on Stack Overflow and was told to move it here. If I am trying to select from two different sets of features for a Gradient Boosting Machine, but I do not want to run through ...
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243 views

Measure the goodness-of-fit in boosted regression tree

What is the apropriate statistic to measure the goodness-of-fit in Boosted Regression Tree (or Gradient Boosting Regression) with continuous response? How can I calculate the coefficient of ...
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265 views

Significance of R Squared in Random Forest / GBM and GBM Tuning Parameters

I often get different level of responses when I discuss about R-Squared and its relevance to measuring the performance of a Random Forest or GBM model. In general, RMSE is a better and more ...
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1answer
209 views

Gradient boosting in R uses only a single variable

I am trying to build a boosting model using the package gbm in R. I have the following code: ...
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220 views

Mean Reciprocal Rank with GBM in R

Let's say I'm optimizing MRR with a GBM in R: ...
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1answer
772 views

Partial Dependency plots and Gradient boosting (GBM package)

Is it possible to plot a partial dependency plot to display the class probability and estimate the effects of a predictor for a GBM model? Something similar to ...
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0answers
74 views

Accounting for seasonality when using gradient boosting

I'm a novice attempting to predict automobile sales using a combination of previous sales (seasonal AR model), macroeconomic indicators such as CPI, consumer sentiment index etc. and more ...
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2answers
578 views

How to choose the number of trees in a generalized boosted regression model?

Is there a strategy for choosing the number of trees in a GBM? Specifically, the ntrees argument in R's ...
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0answers
54 views

How to approach prediction of new observations with incomplete data from model built from complete data

I currently have a gradient boosting model that uses the gbm package in R that classifies observations at the end of a year. Daily behaviors are logged for each ...
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1answer
100 views

How to communicate regression model performance to non-stats people?

In R I created a gradient boosted random forest from 100,000 records with 10 cross validation folds using the gbm library. I want to communicate the strength and ...
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1answer
363 views

Effect of features that are highly correlated with each other on a decision tree

I have a dataset of roughly 500 features and am training a binary classifier using GBM - gradient boosted machines, an ensemble of decision trees. Of these 500 variables, I am sure some are highly ...