Questions tagged [random-forest]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
7 views

Why do Precision-Recall curves stay (almost) the same after classifier calibration?

I was working on a model recently and my output is supposed to be the probability vector of my model (which is a Random Forest). While investigating a bit I learned about model calibration to get ...
0
votes
1answer
25 views

Is increasing the class weight of minority class in Random Forest algorithm decreasing the performance?

I am trying to classify an imbalanced dataset (census dataset with approx. 3:1 imbalance) using Random Forest algorithm in python, and what I observed that increasing the class weight of the minority ...
0
votes
0answers
16 views

How to adress nested data structure in random forest classification [duplicate]

I am trying to predict premature termination of treatment based on symptom change in the first part of psychotherapy (using high dimensional data). I have run a random forest model which performed ...
0
votes
0answers
18 views

Tips on improving random forest predictive accuracy when # of features is really low?

Working on a random forest predictive model with a continuous response variable and two continuous features. Normally when I do RF projects I use some sort of feature selection method to choose which ...
3
votes
2answers
36 views

What type of multi-label method does sklearn's random forest classifier use? [on hold]

I have trained RandomForestClassifier on data with 3 labels. The label set Y looks like this: ...
0
votes
0answers
28 views

Validation Set Accuracy Significantly Higher than Hold out Test Set

I'm building a binary classifier, where each record is a task, and the response variable is whether it was completed on time. I'm using random forest My data set spans from 2000-2015 My hold out ...
0
votes
0answers
13 views

Random forest “out-of-bag” ensemble

I am using the R package RandomForestSRC for random forest applications. In the manual for the main function (rfsrc) they mention a setting called ...
0
votes
0answers
11 views

Precision of decision nodes in RandomForestRegressor

I have trained a RandomForestRegressor in Scikit-learn. I am comparing the prediction of new samples from the trained model versus my own implementation of running through the forest. ...
1
vote
0answers
24 views

Random Forest and Cross Validation

If I use cross-validation for the training and evaluation of a Random Forest model, do I NOT bag the data? If cross validation is used and not bagging (assuming this is a correct approach), are the ...
0
votes
0answers
19 views

On out-of-bag errors for Random Forest

As far as I know, Random Forest can form its prediction rule based on either hard voting or soft voting (based on average predicted probability and is the preferred method) among underlying trees. ...
0
votes
0answers
19 views

Isolation Forest Numerical Example

I'm looking for a proper numerical example to understand Isolation Forests Algorithm correctly. I've read the paper : https://github.com/mgckind/iso_forest/blob/master/icdm08b.pdf, but I want to ...
0
votes
0answers
26 views

Classifying 100 classes

So I'm working on a supervised text classification problem where I need to classify a 100 classes. The way I look at it, I have following approaches- 100 logistic regression classifications with each ...
0
votes
0answers
12 views

Trying to improve the generalization ability of decision tree with bagging or random forest

We are fitting a regression tree on a sample of about 5000 observations with 5 predictors. The data stems from 2 sources. There are reasons to suspect that the tree will not have exaggerated ...
1
vote
1answer
14 views

Finding and using a single (best) decision tree from random forest to evalute a sample [duplicate]

Is there a way that we can find an optimum tree (highly accurate) from a random forest? The purpose is to run some samples manually through the optimum tree and see how the tree classify the given ...
0
votes
1answer
13 views

Retraining model in scikit learn Random Forest

I have a machine learning Random Forest model that predicts a certain variable. It's implemented with scikit learn and it works fine. Now, assuming that the prediction relates to month 1, I need a ...
0
votes
0answers
11 views

decision tree for binary image classification

For a quality inspection task, I need to inspect whether the soldering on a PCB are passing or not. The soldering are at 3 soldering pads that are square shaped. See below. As you can see, there is ...
0
votes
0answers
14 views

Using Random Forest to analyse repeated measures data [duplicate]

I have crop disease data categorized into 2 classes, i.e., healthy and diseased status of the crop. The aim of the analysis is to see how early the disease status can be detected in crop using ...
0
votes
0answers
12 views

A mix of binomial and continuous data in Random Forest Model in R [duplicate]

Is it possible to get meaningful results from a random forest model where the predictors are made up from a mixture of continuous and binomial variables and the response is continuous? Basically, I'...
3
votes
1answer
54 views

How do to deal with data missing NOT at random?

It seems that because values are missing from a specific range of my target variable, my model performs poorly when predicting samples that are actually in that range. My target variable is ...
1
vote
1answer
47 views

How to use just one tree in a random forest with the R package randomForest? [closed]

With the R package randomForest, I can get a forest for classification. With the function getTree(), I can get each tree of the forest. Now, my question is how to do classification for a new input ...
0
votes
1answer
26 views

How do I predict future scenarios after training and validating my model?

Problem I'm new to machine learning and need a little activation energy to get me past this sticking point. I've trained/validated/tuned, and tested a random forest model. Therefore, I've used my ...
1
vote
0answers
37 views

Supervised random forest worked example [closed]

I'm following the first 3 steps for unsupervised random forests from here https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0122705&type=printable The steps are: The ...
0
votes
0answers
7 views

What's the meaning of building classifiers for each class in binary classification?

The question arises when I'm using DistributedRandomForest from the H2O package and find the ...
0
votes
0answers
19 views

Discrepancies in p-values between t.test vs regression after random forest analysis [duplicate]

I was trying to analyze two groups of samples for multiple variables. I first used Boruta (random forest analysis) test to determine the importance of variables in my data. ...
0
votes
0answers
11 views

Confusion matrix vs error curves of “plot.randomForest” function in R

I am building a random forest for a classification problem, using the randomForest package in R. The curves resulting from the plot.randomForest function are not in line with what I get from the ...
0
votes
1answer
26 views

Random Forest yields insanely OOB high error rates

I am using randomForestSRC to fit a random forest of 1000 trees for a 3-class classification problem with 160 cases. I am observing insanely high OOB error rates, sometimes as high as 75%, which is ...
2
votes
0answers
16 views

How to determine the propensity score classifier validity? [duplicate]

I've got a bit more background in machine learning than statistics. Let's say that I want to analyze causal effects based on propensity scores of the treatment and control group. I know that most ...
1
vote
3answers
54 views

Best approach for dealing with continuous predictors with missing data in random forests

I was thinking about a problem I'm facing: I have wage data that I want to add to my model, but it's incomplete (data for about 70% of my observations). So, I was ...
1
vote
2answers
48 views

Can we use SVM and Random forest for classification of one-dimensional data?

I am working on flood inundation mapping using remote sensing data. I am using a single band, a simple threshold value can be used to separate land and water. I am interested in knowing, can we use ...
0
votes
1answer
24 views

Range of Values for Random Forest Mean Decrease in Accuracy

When calculating variable importance using the unscaled (Scale= FALSE) permutation variable importance, what is the range of values you can get? Is it expressed as a percent (e.g. a value of 0.1 ...
0
votes
1answer
30 views

Stratified Sampling on Random Forest

I am working on a project to detect crops from satellite images by prediction. To do so, I use Random Forest model. I discussed with some people about whether to give sample weights on each tree in ...
1
vote
0answers
39 views

To what extent will results change when fitting random forest structurally vs in batch?

I am facing a problem where I am not sure how to apply the random forest algorithm. Suppose my target variable is $Y$ and I have some $X_1, X_2, \ldots, X_p$ as predictors. I am applying the random ...
0
votes
0answers
6 views

Multicollinearity in random forest [duplicate]

Is a multicollinearity of predictors anyhow problem in random forest or decision trees?
0
votes
0answers
12 views

Big difference in random forest test subset error and other subsets

I'm using R "randomForest" package for predicting stock prices. I have more than 3000 observations and 90 columns. I have excluded my last 150 days from data set and divide the rest of my data to ...
3
votes
0answers
67 views

Mathematical definition of the variable importance measure 'increase in node purity' from R randomForests package?

I'm trying to wrap my head around the concept of variable importance (for regression) from the randomForest package in R. I'm trying to find a mathematical ...
1
vote
1answer
35 views

Prediction problem in randomForest in R [closed]

I'm trying to use the randomForest algorithm in R. When I apply the algorithm to my database, I got the following result: ...
0
votes
1answer
35 views

Adjusted r-squared and regression without an intercept

I am using R^2 and then computing the adjusted R^2 in cases like linear regression that use an intercept and the regression line does not necessarily passes through the origin. Lately, I've been ...
1
vote
0answers
43 views

OOB error prediction in RF if case weights are used

I have a dataset for which grossing-up factors are given. I am using these factors as case weights for a random forest (R package ranger). Until now I was using the OOB prediction error for tuning, ...
0
votes
0answers
17 views

Random Forest Underperforms Median on Training Set for Toy Regression Problem

I have found that random forests is failing on a toy regression problem. My prior impression of random forests is that it is very robust, so I expected that, on the training set, it should always ...
2
votes
2answers
79 views

How to Reduce Number of Variables Before Running Random Forrest or XGBoost

I've simplified the problem I'm working on for this post, so that the focus is on the issue I'm having. I'm trying to predict if a patient will be diagnosed with arthritis in 2019, based on the ICD-...
3
votes
2answers
110 views

Why does more variables mean deeper trees in random forest?

I'm looking at the depth of trees in a random forest model, using the randomForest and randomnForestExplainer package in R. The ...
1
vote
1answer
24 views

scikit-learn IsolationForest no variance feature

I'm using IsolationForest algorithm in order to detect anomalies in my data and to use this model to detect future anomalies in new rows and came across a few questions: Is the model good for ...
0
votes
0answers
13 views

randomForestOOB Error Rate Diverging with > nTrees

I am in a preliminary stage right now with classifying churned customers and have an interesting oob error rate chart that I would really like to get a better understanding of from someone more ...
0
votes
1answer
70 views

Gradient Boosting with Random Effects [duplicate]

I want to run gradient boosting regression on a dataset whose rows are not independent. Specifically, the rows are clustered, and you could consider the clustering variable to be a random effect. ...
1
vote
1answer
35 views

Predictions based on k-fold Cross Validation, which model is used (Caret)

I am sorry if there is an obvious or intuitive answer to this, which I missed. We have tuned the hyperparameters of a RF using Grouped 10 - Fold CV (repeated 5 times), to obtain the values for mtry ...
1
vote
0answers
41 views

Difference between BalancedRandomForestClassifier and sklearn.ensemble.RandomForest + RandomUnderSampling [closed]

What is the difference between fitting training data with imblearn.BalancedRandomForestClassifier compared to using sklearn.ensemble.RandomForestClassifier + imblearn.under_sampling.RandomUnderSampler ...
0
votes
2answers
53 views

Why don't we use regularization on decision tree split?

I heard people ask which one is better: Linear regression with regularization or Random Forest. My question is why can't you use regularization with Random Forest? My understanding is that different ...
0
votes
0answers
8 views

Classifier for detecting abnormal noise using FFT

As a simple machine learning application for quality evaluation, I want to detect abnormal sound in a product. A good product would make a signature humming sound, a bad product might make noises due ...
0
votes
0answers
7 views

How to compute the prediction performance/error of the L1 split in random survival analysis?

I'm trying to compute a prediction performance for the L1 split, which is a custom-built model by Hoora Moradian, Denis Larocque, and François Bellavance (L1 splitting rules in survival forests, 2015-...
0
votes
0answers
21 views

Small sample size with very skewed right response [duplicate]

I have a dataset of 300 observations with 7 predictor variables with 1 continuous response variable. The response is strongly skewed to the right and there are no significant correlations among any ...