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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Same prediction result with little probabilities change
I build a job prediction system leveraging data scrapped from LinkedIn with Random Forest and compared to XGBoost. XGBoost was used due to high accuracy after training.
When I made a prediction, I ...
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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 ...
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How much is the acceptable percentage for Random Forest in Landslides prediction?
RF had been developed to overcome overfitting in decision trees but in some cases RF still experiences overfitting in landslide prediction, which varies from 2% to 12%. How much overfitting is ...
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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 ...
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Modeling the previous inputs to affect next output in Machine learning
I am working on a dataset contains one output variable and a number of input variables.The data looks like the following:
Y, X1, X2, X3, X4
7, 5, 0.7, 8, 9
3, 6, 0.3, 9, 9
....
Where Y is the output ...
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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. ...
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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 ...
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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 ...
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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 ...
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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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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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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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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 ...
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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?
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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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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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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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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 ...
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