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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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Does a random forest classifier always get 100% accuracy on its own training data?

Due to the way that decision trees work, do random forest classifiers always get 100% accuracy on their own training data? My random forest classifier got 100% accuracy on its own training data, so I'...
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Does a Random Forest leaf give a specific output for a specific class in a multi class random forest

I am trying to understand the basics of random forest machine learning applied to vegetation classification. Do I understand correctly that the output/leaf node of each individual decision tree ...
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What model is good for learning both within and across categories?

How can I incorporate both general trends and subcategory-specific trends into a model? Let's say I am predicting factors that affect import volume, for example. There are many industries which have ...
BigMistake's user avatar
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Non constant Feature Importance

I have a financial dataset which has 10 years worth of data. The aim is to build a regressor capable of predicting next year sales. So, if I want to predict sales for 2024, I could use data from 2023, ...
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Same prediction result with little probabilities change

I built a job prediction system leveraging data scrapped from LinkedIn with Random Forest and compared it to XGBoost. XGBoost was used due to its high accuracy after training. When I made a prediction,...
ezaryf's user avatar
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How to make a RandomForestRegressor learn to differentiatie similar inputs with different outputs?

I'm working on a regression task with Sklearn RandomForestRegressor and I'm having some trouble distinguishing between two similar data with very different expected outputs. For example, each pair of ...
Luís Henrique Bandória's user avatar
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How to prevent machine learning to learn misleading correlations

I am currently training a Random Forest model on about 400 features per instance. The training ROC was about 0.95 which is pretty high I think. However, when visualizing the variable importance of the ...
Ai4l2s's user avatar
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Why does GridSearchCV model give worse results despite same parameters used with base model

I am trying to make prediction using random forest regression and then utilize GridSearchCV to tune hyperparameters(just 'n_estimators'). However results of GridSearchCV are worse than base model. ...
dancineer's user avatar
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How do I know if my Random Forest Regressor Model is overfitted?

Im creating a Random Forest Regressor Model with a small dataset (30 data points). I tried with other models but RF was the best one, however, after applying GridSearchCv I got that the training set ...
Gaby's user avatar
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1 vote
2 answers
300 views

Machine Learning Models for Longitudinal Data

Recently, I had the following question about supervised classification models (e.g. random forest) for longitudinal data. Suppose I have the following data about students passing a fitness test - the ...
stats_noob's user avatar
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1k views

Random forests - are more estimators always better?

I'm learning about more advanced methods of hyperparameter optimization, such as the Bayesian methods in the scikit-optimize package. For those unfamiliar with the ...
SuperCodeBrah's user avatar
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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 ...
lari1995's user avatar
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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. ...
user101464's user avatar
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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 ...
Banik's user avatar
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1 answer
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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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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 ...
spiridon_the_sun_rotator's user avatar
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1 answer
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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?
jaiswati_b's user avatar
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1 answer
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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 ...
Swakshar Deb's user avatar
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2 answers
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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 ...
stoic-santiago's user avatar
2 votes
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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 ...
notarealgreal's user avatar
4 votes
2 answers
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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 ...
jennifer ruurs's user avatar
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1 answer
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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 ...
jennifer ruurs's user avatar
2 votes
1 answer
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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 ...
jennifer ruurs's user avatar
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1 answer
98 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?
jennifer ruurs's user avatar
1 vote
1 answer
102 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 ...
jennifer ruurs's user avatar
10 votes
1 answer
5k 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?
mallea's user avatar
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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?
Iram Shah's user avatar
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2 votes
3 answers
184 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 ...
imflash217's user avatar