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

learn more… | top users | synonyms

3
votes
2answers
51 views

Random forest and LASSO regression both give different variable importances

I have a dataset with 163 observations (all countries in the world with population > 1000000) and 290 variables related to their disease burden and performance. Because there are more variables than ...
0
votes
1answer
3 views

Do proximity or importance influence predictions by a random forest?

Does proximity or importance influence in prediction quality in random forests? Or are they just information about the model? Setting them to True does increase accuracy? Does this model: ...
3
votes
1answer
29 views

How important are parameters in random forest regression?

So I'm using random forest regression to predict on some data and I usually go with 25 or 50 trees and 50 random features. Currently I have only one data set to care about, but in the future more will ...
0
votes
2answers
56 views

adjusting random forest outputs built from 50:50 sample back to balance of total population

I'm using a random forest in R (randomForest) to predict a binary output (1,0) for a dataset that is heavily unbalanced. In this example let's assume the population has 1% 1's and 99% 0's. Building ...
1
vote
2answers
47 views

What are the model parameters and hyperparameters of Random Forest classifier?

The parameters required for a Random Forest classifier are as follows: Depth, $d$ No. of random features, $K$ No. of trees, $I$ Randomizer seed, $R$ Which of the above are hyperparameters and ...
0
votes
0answers
14 views

What is the effect of class weight in Random Forest/Extra Trees Classifier variables importance?

In the sklearn implementation of random forest and extra trees classifier a class_weights parameter is available http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier....
0
votes
0answers
25 views

How to handle missing values in a Random Forest model?

I have a data with a binary target variable and some predictors. I tried running a random forest model and failed. First of all, I found it hard to enter all predictors to the model. I did: ...
1
vote
2answers
66 views

How do I compare the performance of random forests for regression?

I've trained two random forests for a regression, based on the same data and variables, only with a different number of tree in each one. If I'm not wrong, having more trees is always better in terms ...
0
votes
0answers
14 views

Long vector error in R RandomForest for a small dataframe

I am trying to run a RandomForest on a dataset with 390343 rows in R with the randomForest package.I am getting this error : Error in randomForest.default(m, y, ...) : long vectors (argument 24) ...
1
vote
1answer
55 views

Using Random Forest Variable Importance to train SVM models (R)

I have trained a Random Forest model in R with the caret package but the results are not very promising. I have decided to try with SVM models but I have a great ...
0
votes
1answer
50 views

Interpretation of feature contribution in random forest

In the featureContribution function (in the R package rfFC), what should we interpret if all the scores for all the features are ...
0
votes
0answers
25 views

Probabilities of classes using h2o.predict

How does h2o.predict calculate the probabilities of different classes ? In the randomForest package of R, the probabilities are calculated based on the number of votes. For example probability of ...
0
votes
0answers
22 views

Difference between votes and probability in predict.random forest

Could some one help me understand what is the main difference between votes and probability in the type argument of predict.randomforest? My understanding is that if the individual trees are grown to ...
0
votes
0answers
27 views

Interpretation of a strange part in ROC curve

I generated the following ROC curve using Rapid Miner to compare few binary classifers but I don't know how to interpret the curve of "Random Forest" model It doesn't look like a curve
0
votes
2answers
29 views

choosing a model after feature selection process

so ive been selecting features for a regression problem and have obtained a list of the best performing feature sets. (note my list is actually several thousand lines long) 188.493 186.989 [379.45, 0....
1
vote
2answers
86 views

Speed of prediction: neural network vs. random forest?

I'm currently trying to improve on a classifier. The current method used is a neural network, and the method I've found to be better is a random forest (or even just a single tree). With 40 trees, the ...
0
votes
1answer
25 views

Measuring Variable Effect in Random Forest Regressor

Is there a way to measure the effect individual predictors have on an outcome for a Random Forest Regressor? If there's not something similar to a regression coefficient, is there a way to utilize ...
0
votes
0answers
18 views

What can be said about a dataset if logistic regression outperforms random forest classifiers on test set?

There is a dataset with about 20 dimensional input vectors and binary output vector. There are about 100K training examples. When trained using logistic regression the accuracy on training set is ...
0
votes
0answers
18 views

When do you use random forest over decision tree?

What criteria make you decide to use random forest over decision tree ? How do we decide when decision tree is not sufficient ?
0
votes
0answers
8 views

Random forest block observations bootstrap samples / OOB observations

I would like to use random forest regression for prediction of schizophrenia-related continuous measures from genetic data. However, I have siblings in my data which would still be problematic with ...
0
votes
2answers
60 views

Why Does a Variable with Weak Correlation to Outcome Variable Emerge as Most Important Factor in Random Forest?

I'm puzzled about why a dependent variable with the weakest correlation to the outcome variable emerges as the most important factor when I run my random Forest on the same dataset. It beats out ...
0
votes
1answer
50 views

Are numerical variables must for random forest algorithm?

I am trying to find the variable importance on a credit scoring database. I have categorical inputs as well as numerical inputs. My question is does the random forest algorithm works the same way when ...
1
vote
1answer
46 views

Scalable Random Forest For Massive Data

My problem is simple. I want to train a dataset using random forest on a huge dataset (with $n$ rows). Let's assume I can only fit $b < n$ rows in memory at a time. Model Choice I see several ...
0
votes
1answer
23 views

plot accuracy of classification in Random forest and SVM in R

I have a question and I will be grateful if you help me. Is it possible to plot admixture or pure of individuals with Random forest-SVM methods (models) in R . Is there command for this work?
1
vote
1answer
43 views

References for learning about online random forests

I am new to concepts of random forest. Can someone provide relevant sites where I could get learn more about using random forests to learn incoming data like an online algorithm?
-1
votes
2answers
65 views

lasso regression on top of random forest

Some time ago, I found a paper describing usage of lasso/elastic net regression on binary variables come from random forest. In short, (i,j)-th variable takes 1 if given observation belong to leaf no. ...
0
votes
1answer
37 views

Random Forest in R - most important variable causing errors

I'm trying to fit a Random Forest model in R. I've got a DF with 13 factorized variables plus the target one (binary). When I try to use all the variables with ...
0
votes
1answer
69 views

ROC Curve for different classifiers

I am trying to compare the classification performance of different classifiers. So far, I am using SVM, Random forest, Adaboost.M1, and Naive Bayes. 70% of data is used as training (and then plotting ...
0
votes
0answers
41 views

Machine learning approach when facing low predictive power features

My dataset has 3.6k samples and 600+ one-hot encoded features. Each feature has between 5-2000 instances, averaging around 150. Intuitively, I don't believe that my features should have much ...
0
votes
0answers
15 views

MSE and %var(y) in randomForest

I'm making my first trials with randomForest and I have a question about the output of the do.trace = TRUE parameter. It gives ...
1
vote
1answer
44 views

the trees in a Random Forest

What I want to ask is as follows: Is it possible that there are many identical trees in a Random Forest? If there was very little different training data in a tree sample by bootstrap aggregating, ...
0
votes
0answers
17 views

Is the offset_column parameter in H20's random forest algorithm the same as the offset option in SAS Proc Logistic?

I am trying to use H2O in R to run a random forest. http://docs.h2o.ai/h2oclassic/Ruser/rtutorial.html In the documentation, I saw that there is an option for an offset parameter but I cannot find ...
0
votes
0answers
22 views

Ranking of the prediction

I am currently using random forest to predict fraud on contracts. Each day, we predict the contract of the past day. The problem is, the guys I am working with keep telling that the value we should ...
0
votes
1answer
49 views

Training and test sets in random forest regression

Why do I keep reading about specifying training and test sets with random forest regression? As probably obvious from this question I am new to this method, but what I thought one of the cool things ...
0
votes
0answers
7 views

Dataset dimension with ExtraTrees

We are using an ExtraTrees with Fitted Q-Iteration, and testing it with different size of dataset, all generated in the same way. FQI seems to have very good performance with small dataset, but it get ...
0
votes
1answer
26 views

Convert categorical data with large number of levels to numeric data and what kind of mapping to use

I have a dataset with 20,000 rows and 11 columns. Out of the 11 columns 10 are categorical. Out of the 10, three have very large number of levels. i.e. levels >60. One of the variable is basically ...
0
votes
1answer
22 views

How do GBR trees differ from random forests regression in terms of predictive performance?

Is there a case when one would use gradient boosted regression trees instead of random forests regression (or vice versa)? It appears gradient boosted regression trees have done far better in ...
0
votes
2answers
46 views

Is feature engineering relevant at all for Random Forests?

Random forests is an ensemble of trees that learns the hidden patterns in the data. I have mostly tried doing some feature-engineering before running the Random Forest model but is it required or the ...
0
votes
0answers
10 views

RandomForest undersampling non events

I have data that is about 50 to 1 nonevent to event (in this case, purchases). I was taking an equal, or close to equal, sample of nonevents to events with a random forest model, and running the ...
0
votes
0answers
20 views

Need new strategy for single class classifier

I am attempting to create a single class classifier where the classes are fairly close to balanced (+/- 25). My dataset has ~2,800 samples and ~1,100 features. All of the features are binary except ...
1
vote
1answer
51 views

In a random forest algorithm, how can one intrepret the importance of each feature?

I am in the process of building a Random Forest algorithm in MATLAB using the TreeBagger function. In the documentation, it returns 3 parameters about the importance of the input features. My ...
0
votes
0answers
33 views

Random Forest: Strange Feature Importance Results for Constant Variables

I've been using the RandomForestClassifier in python's Sklearn package to assess the importance of the features in a large dataset with features that are both binary and continuous. I've done quite a ...
0
votes
3answers
45 views

Multiclass classification question

I am working on applying Random Forests to a multiclass classification problem, where I have a set of 11 predictor variables and a response that can take the values of "Yes", "No", and "Maybe". In my ...
1
vote
1answer
44 views

Time Series forecasting with useful predictor variables

I am playing with time series data related to a issue ticketing system. The system logs all open tickets at any one point and my task is to predict what the volume of open tickets will be in 5,10,15 ...
0
votes
1answer
17 views

Out of Bag Prediction Error Estimate from Random Forest Regression (i.e., not classification) [closed]

I would like to use the OOB cases from a random forest fit to estimate the mean squared prediction error so I don't have to cross-validate. I am using the randomForest package in R. It is clear from ...
0
votes
0answers
13 views

How can one quantify the variable importance dilution effect in random forests (and similar statistical learning methods)?

In Applied Predictive Modelling (Kuhn, Johnson, 2013, p 202), the authors refer to a dilution effect whereby compared to a single tree or a classical regression technique, the difference in importance ...
0
votes
0answers
18 views

Create a predictive user model

I am a bit lost with creating a user model in R. I would like to create a model that predicts whether a user is likely to do an action or not, based on data on his past behaviour (Target variable ...
2
votes
1answer
60 views

In random forest, what happens if I add features that are correlated?

(Sorry for the potentially unclear English, not a native speaker) I'm training a random forest, trying to predict market shares of future stores on geographical areas. I have many features for these ...
0
votes
1answer
62 views

Random Forest model good train and test performance but bad “real world” performance

I am working on a classification problem where I need to classify objects based on a visual data. There are a couple hundred different classifications to be made and I have around a million plus ...
0
votes
0answers
37 views

How to know the importance of all the variables and levels in them using Random Uniform Forest in R?

I have a dataset containing 3 parameters (Region( factors - say US,UK,Aus,NZ),Domain or Industry( factors - say IT,Electrical,Mechanical) and Scope - good or bad). Using Random Uniform Forest package ...