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Random forest is a machine-learning method based on combining the outputs of many decision trees.

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RandomForest regression p-Value

Dear smart people of the internet, I'm currently working on a data set (a regression problem) and compare OLS vs RandomForest explanation power. Working with p-Values of those regressions would be ...
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What are the disadvantages of Random Forest Algorithm? [duplicate]

I am using random forecast algorithm via python sklearn library to forecast data. So far it's accuracy on my training data is good. I am using the algorithm to find the predict a decision based on ...
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1answer
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Random Forest plot Interpretation in R

I am analyzing data (which I am unable to share), and created several classification models between four classes using the randomForest() function. They are fairly ...
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19 views

Variance partitioning in Random Forest

I'm wondering whether it's possible to perform variation partitioning among groups of variables in Random Forest (regression), similar to how one would partition the total sum of squared deviations of ...
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Forms of interaction between variables Random Forests take into account

What forms of interaction do Random Forests take into account? I understand from other posts ( Including Interaction Terms in Random Forest ) that Random Forests take into account interactions like x1 ...
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1answer
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Sample size for designing a study which creates a classifier

I have to plan a study in which I will have to create a classifier. The output variable is a binary with an estimated proportion of value 1 in the overall population of interest to be 0.10 (and ...
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1answer
16 views

Random forest fully fits training sample

Using the randomforest package in R, I am getting 100% accuracy on the training dataset. Here is a reproducible example : ...
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56 views

Random Forest Variable Importance Differences by Number of Pseudo Variables

I recently worked on a project that involved building a supervised model where we started with hundreds of variables in our data set. Our end result was to be a simple model, but we needed a quick way ...
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1answer
52 views

Can I combine many gradient boosting trees using bagging technique

Based on Gradient Boosting Tree vs Random Forest . GBDT and RF using different strategy to tackle bias and variance. My question is that can I resample dataset (with replacement) to train multiple ...
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1answer
31 views

Special values in continuous numerical variables/features in Random Forest

I have a binary response variable I am seeking to predict using Random Forest. I have a sizable dataset of 150k rows, I have about 200 independent variables or features to use to model the outcome. ...
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58 views

Random forest variable importance: Mean minimal depth and number of nodes disagree

I'm trying to determine variable importance for a random forest with 8 predictors, and different variable importance measures are telling different stories. The forest was generated in R with ...
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69 views

About Partial dependence for Poisson GLM

Can someone tell me what would be the expression for calculating the partial dependence on a GLM model with family specified as Poisson? From applying Friedman partial dependence estimation ...
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1answer
12 views

Leveraging Images in Random Forest Predictive Model

I am using a random forest to make numerical predictions for the performance of products using structured variables, and am looking to leverage images to improve my predictions. One idea I have is to ...
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1answer
37 views

CART on Timeseries Forecasting

I've read in a few articles where it was talked about using CART for timeseries forecasting and anomaly detection. However, I would want to remove the Seasonal and Trend noise in my temporal data. I'...
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Poor P-R curve for binary classifier trained on balanced data, with imbalanced test data

I have a very imbalanced dataset (9:1), for which I have performed under-sampling and achieved a balanced training set (~130k samples total post balancing). I am performing classification using ...
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16 views

Why do partial dependence plots look different with same model but different data?

I've got a randomForest model object from the R package randomForest and am using the function partialPlot to generate partial dependence plots. I know that normally one would create these using the ...
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31 views

Can unbalanced classes introduce bias in a Random Forest model?

I am working on a classification problem using Random Forest. The training set has 600 instances and 16 attributes. The final class is an Yes/No answer. The ratio of "Yes" to "No" in the training ...
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1answer
25 views

Using Random Forest Regression vs Classification for Confidence Metrics

I have a fairly large dataset with each sample corresponding to one of two target values. I'm using a random forest to assign confidences to new samples for what class they may belong to. My question ...
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20 views

multicollinearity and its effect on random forest classifier? Please comment [duplicate]

If the input variables exhibit multicollinearity then how does it impact random forest classifier?
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1answer
31 views

All the trees from randomForest-model are use all the attributes of object, but not only mtry?

I have trained model for classification task in R using randomForest - "RF_Model" ...
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1answer
27 views

Fine-tuning in Random Forest regression analysis for forecasting

I am trying to run a regression estimation on random forest algorithm. As I am very inexperienced in using RF algorithms, I couldn't figure out some questions unless I make a quick survey on ...
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1answer
29 views

Using machine learning model for predictions

I am trying to use my random forest model for predictions. I only want to select important variables(ex:top 50), and use the saved RF model to predict the response variable, changing the predictor ...
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3answers
568 views

How to make the randomforest trees vote decimals but not binary

My question is about binary classification, say separating good customers from bad customers, but not regression or non-binary classification. In this context, a random forest is an ensemble of ...
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1answer
15 views

Variables with zero variation in time series random forest estimation

I am trying to build a forecasting model for three products. Because I have only 20 -25 daily readings per product sales and these series have gaps during the time span of analysis, I switched to the ...
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what is difference between Random Decision Forest by Tin Kam Ho and Random forest?

Can anyone tell, these two algorithm are same or have some difference
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1answer
38 views

Retrain random forest with important variables

So I have a classification problem with around 2000 predictors. First I run a random forest model to get important variables. Then I only use those variables (let say the top 30) to run the model ...
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In a decision tree, what do 'samples' mean?

On the decision tree diagram below, what do the 'samples' mean and how do they relate to the 'value' line that follows?
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Feature importances in random forest

I'm using the random forest classifier (RandomForestClassifier) from scikit-learn on a dataset with around 30 features, 3000 ...
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15 views

Positive or negative impact (R: randomForestExplainer) Random Forest Explainer

Found on the internet an interesting package for R, called "randomForestExplainer", see: RandomForestExplainer in R It shows the most important variables (by for example: showing the minimal depth of ...
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In Random Forest, why is a random subset of features chosen at the node level rather than at the tree level?

My Question: Why does random forest consider random subsets of features for splitting at the node level within each tree rather than at the tree level? Background: This is something of a history ...
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which models' variable importance should I trust if their rank are different?

I have a question about vairable importance that is generated from different models, say random forest and logistical regression. For example, if I have two models that are trained on the same ...
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1answer
17 views

Oversampling problems in prediction

I have a dataset that contains 284315 samples of class 0 and 492 of class 1. I know, that's huge. I heard about oversampling methods, so I did the following using the RandomOverSampler library: ...
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2answers
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Alternatives to Random Forest's Feature Importance for choosing best features [closed]

I have already established method using R's Random Forest tools for ranking most important features; for binary classification task. I'm looking for other methods for doing the same task. So that I ...
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1answer
41 views

Regression Trees: how to split if node has 2 samples

Sorry, but this is not a general question, so i am going to be as specific as i can. I have searched a lot, however i cant consider the following case in regression trees: The following picture shows ...
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1answer
41 views

High complexity random forest always performs best on test data

(I am new to machine learning so please bare with me) I am using Random Forest Regression algorithm but I am seeing interesting results. I randomly split data into validation set, test set, and ...
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0answers
11 views

significance of random forest variable importance via multiple running or permuation of variable value

I am trying to select variable from random forest results based on importance. Variables with higher importance value are favorable. There are at least two ways to access significance of selected ...
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Average Precision or FBeta & Decision Threshold Tuning for Binary Classifier [duplicate]

I'm working with an imbalanced binary classifier data set (3% positive) in sklearn. The cost of a false negative is extremely high so recall is much more important than precision. To baseline my ...
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20 views

Interpretation of variable importance in Random Forest

I'm currently using Random Forest to train some models and interpret the obtained results. One of the features I want to analyze further, is the variable importance. The thing is I am not familiar on ...
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22 views

What happens when the feature importance plot is dominated by only one feature?

I got a feature importance plot from my gbm model, where one of the feature shows a very high value of feature importance as compared to the other variables. Will that be affecting my predictions in a ...
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0answers
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Decrease the variance of ExtraTrees Classifier

I am trying to solve a machine learning problem. I am using ExtraTrees Classifier. When I am plotting the learning curves, I can see a wide gap. I need to decrease that (variance). I read about ...
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1answer
79 views

What happens when bootstrapping isn't used in sklearn.RandomForestClassifier?

I've been optimizing a random forest model built from the sklearn implementation. One of the parameters in this implementation of random forests allows you to set Bootstrap = True/False. While tuning ...
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36 views

Selection of random forest regression models based on r2_score

I'm making a regression model which predicts the concentration of air pollutant. It consists of the following features: Features Things that I have done so far : Assigned mean values to the missing ...
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What is the difference between Recursive Feature Elimination and Backward Pass Feature Elimination

In the case of the Backward Pass you eliminate the features starting with the ones that have the smallest Pearson correlation with the predicted parameter. You keep the parameter if after its ...
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1answer
82 views

Improve the precision of random forest for count data

I am trying to create a classification model that predicts whether a customer will enquire for a financial product based on some 250 independent variables. 98% of the variables are count variables and ...
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22 views

AUC from test set higher than training set using GridSearchCV + Random Forest Classifier on Oversampled Dataset

I was trying to compare the effect of running GridSearchCV on a dataset which was oversampled prior and oversampled after the training folds are selected. The oversampling approach I used was random ...
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1answer
100 views

H2o interpretability - LIME

I have trained a model to predict heart attacks using random forest algorithm using H2O. I have good performance in cross validation. Now, I want to give more interpretation to the predictions in a ...
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1answer
42 views

Precision and recall of imbalanced classes

I'm new and have searched many questions about this problem in this stack, but those answers aren't clear enough for me. The point is the area under PR curve of my binary classes is the same as the ...
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How to check if the output from different Random Forest regression models is significantly different?

I have produced a series of random forest regression models (13 in total) and would like to check if the models are significantly different. I have already compared the models using summary statistics ...
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1answer
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sklearn: Is it possible to implement model metrics on Random Forest without creating a separate test set?

I have created a random forest for binary classification. I have a very unbalanced dataset (taken from the actual distribution of the data the model will predict) - tens of thousands of negative cases ...
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24 views

Interpretation of random Forest result obtained from 'randomForestExplainer' package

I am using Random Forest analysis on my data set where all the variables, both X and Y, are categorical in nature. I have obtained very interesting results of analysis using randomForestExpaliner ...