Questions tagged [random-forests]

For questions related to the random forest (or random decision forests), which is an ensemble machine learning technique (that is, an ML technique that uses or combines different models).

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Derive information for sub-scoring from one scoring model

I am currently working in Python with a random forest algorithm to perform a scoring. My output is binary. The idea now is to derive sub-scores from the above model that give an opinion on different ...
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17 views

proof of convergence for the random forest algorithm

I am looking for the proof of convergence of the random forest algorithm. A cursory google search shows many, but I do not understand which version (original?) of the algorithm this is. Can you kindly ...
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1answer
47 views

Reinforcing Learning when action has no effect on the environment

I am trying to get my head around a problem where the action by the agent can not change the environment. Without going into details, my problem is about error correction in an stochastic environment. ...
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1answer
19 views

What approach would work well for predicting earthquake intensity based on historical data?

My problem: I own warning system where I collect data from institutions and send them over through various ways to users. I would like to hear your advice on what approach I can use for solving my ...
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1answer
33 views

How do I take the correct classification predictions of an ml algo (i.e. random forest/neural net) and sort the instances in each category?

I am trying to sort the instances within each of 5 classification categories in a dataset that has been put through both a random forest classifier and a neural network with 99% accuracy on each. ...
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16 views

Is there a theoretical or recommended number of estimators for Random Forest in Feature Selection?

I am using a RandomForestRegressor as an estimator in the SelectFromModel object (sklearn) ...
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51 views

When do the ensemble methods beat neural networks?

In many applications and domains, computer vision, natural language processing, image segmentation, and many other tasks, neural networks (with a certain architecture) are considered to be by far the ...
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1answer
73 views

What are some applications where tree models perform better than neural networks?

Neural networks are known to be generally better modeling techniques as compared to tree-based models (such as decision trees). Are there any exceptions to this?
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43 views

Can I apply AdaBoost on a random forest?

I know the random forest is a bagging technique. But what if my random forest overfits on a dataset, so I reduce the depth of the decision tree and now it is underfitting. In this scenario, can I take ...
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2answers
769 views

Why are decision trees and random forests scale invariant?

Feature scaling, in general, is an important stage in the data preprocessing pipeline. Decision Tree and Random Forest algorithms, though, are scale-invariant - i.e. they work fine without feature ...
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0answers
72 views

How can I classify instances into two categories and then into sub-categories, when the number of features is high?

I'm working with a problem where I have a lot of variables for different cases of different users. Depending on the values of the different variables of a concrete user in a concrete case, the ...
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2answers
63 views

Oposite type of predictions for unbalanced dataset

I have a big dataset (28354359 rows) that has some blood values as features (11 features) and the label or outcome variable that tells whether a patient has a virus caused by a Neoplasm or not. The ...
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1answer
69 views

How could decision tree learning algorithms cope with imbalanced classes?

Decision trees and random forests may or not be more suited to solve supervised learning problems with imbalanced labels (or classes) in datasets. For example, see the article Using Random Forest to ...
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1answer
54 views

How can I determine the bias and variance of a random forrest?

On this website https://scikit-learn.org/stable/modules/learning_curve.html, the authors are speaking about variance and bias and they give a simple example of how works in a linear model. How can I ...
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1answer
72 views

How to interpret this learning curve plot

Bellow I have a Learning Curve plot How should I interpret this plot for my random forrest algorithm (the second one the most complex one)? Which one is the best?
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1answer
69 views

How should I interpret this validation plot?

Bellow I have a validation plot How should I interpret this validation plot? Is my data underfitting? What else can be seen from this? Which one is the best? What does it mean that the right line is ...
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1answer
2k views

Are decision tree learning algorithms deterministic?

Are decision tree learning algorithms deterministic? Given a fixed dataset, do they always produce a tree with the same structure? What about the random forest?
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1answer
58 views

How many trees should be generated in a random forest?

What are ways of determining the number of trees to be generated in a random forest algorithm?
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3answers
141 views

Why are tree-based models more widely used in Medical Diagnosis?

In Chapter 14.4 (p. 664) of the book Pattern Recognition and Machine Learning by Bishop, it is mentioned that tree-based models are more widely used in Medical Diagnosis. Apart from giving better ...