Questions tagged [random-forest]
Random forest is a machine learning ensemble method based on choosing random subsets of observations and variables for each of many decision trees.
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Why is my random forest regressor more likely to under-predict when making larger predictions?
I've recently built my first regression model, using scikit-learn's random forest regressor. The predictions are rounded into one of 12 segments; these begin at £15 and progress in £5 increments up to ...
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How to approach Machine Learning with Circular distribution (Classification problem)
I have 41 continuous columns and all are distributed roundly to each pair:
I used:
SMOTE for resampling data ( my dataset is imbalanced)
Test dataset: last month in the data dataset. Train dataset: ...
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The accuracy of a random forest algorithm is nearly 1, how do I solve this problem? (with updates)
I have a problem with a random forest algorithm, I'm firstly explaining the situation and then I'll ask questions.
I have a dataset of 10000 raws x 40 columns, 39 of them are features and 1 contains ...
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Machine Learning Binary Classification Model on a Small Tabular Imbalanced Dataset - Improving Performance
I have a dataset that is fairly small (15,000 rows), with 10 features for a model to learn from. It is not possible to increase the size of this dataset. I am using machine learning for binary ...
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Identifying patterns and trends in insurance behavior with clustering or decision trees
I have a list of patient accounts based on their discharge date. I have various inputs related to each patient such as their financial class, insurance information, demographics, claims information, ...
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Very low AUC in the test data while having adequate accuracy on the test data in Random forest model
I am encountering a strange issue where I obtained low AUC = 0.18 on a test data whereas the model I built gave an accuracy of 80.3 on this test data, and sensitivity of 55.5 and specificity of 90.7
I ...
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RandomForestClassifier OOB hyperprarameter
I am currently working with a RandomForest Classifier and I have conducted a RandomSearchCV to find optimum hyperparameters.
Now, I wanted to investigate in the OOB error and I am wondering If I plot ...
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Using a prediction from a First Principles Model in a second, statistical model to improve accuracy
I am trying to figure out what this is called, so I can do some reading on it and see which types of statistical models excel in this framework (and known pitfalls to avoid). I have been calling it a ...
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Cumulative feature importance in Random Forest taking into account past days data
I have a dataframe with past days data and current day data. Example columns [ cases , mobility, temp , rh , cases_1, mobility_1 , temp_1 , rh_1, cases_2, mobility_2, temp_2, rh_2 and so on. . ]. My ...
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Only one node generated after using decision tree model on training data set
I am trying to build a decision tree model predicting an outcome variable (named : Results) based on predictor variable. Indeed, I have applied one-hot encoding on some of the ">2 level" ...
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Multi-class Classification with Categorical and Time-series Features
I have a problem where I want to classify certain entries into clusters, based on their categorical features (such as Country, Category, as dataframe columns), and also their selling pattern (time ...
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Interpreting the SHAP values presented in layered violin plot in SHAP-library for Scikit binary RandomForestClassifier
I am using the SHAP-library for computing feature Shapley values for a binary RandomForestClassifier which has naturally two outputs, 0 or 1. The forest itself consists from 100 decision tree ...
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The best algorithm(s) for finding the best hyperparameters (special case)
I would like to ask for help with the following.
Given the following dataset, which I have split into train and test sets:
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Average training instances sampled with bagging
The book Hands-On Machine Learning has a section on Out-of-Bag Evaluation related to Decision Trees, where it's stated that,
By default a BaggingClassifier samples m training instances with ...
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Strange behaviour on Random Forest Classifier
I've build two identical rf_classifier and trained with two identical datasets but with 2 different target variable (the sell or not sell of two different specific products, one for each algorithm).
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How to get Shap feature importance values for a random forest classifier at local level
I have a random forest classifier (binary) model that I'm using to run prediction on unseen test data. For each observation in the test data, I want to get the feature importance (which feature was ...
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How do I interpret probability results in conjunction with my known precision/accuracy/recall scores?
I have a Random Forest Classifier (trained with sklearn) modeling a binary data set. Here's what the configuration looks like (I've tuned it for precision intentionally):
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Random forest regression model for stock price prediction output has a flat line in the predicted values during the initial values
I have a random forest regression model for predicting the close price for stock data. I am getting model accuracy as like this:
/n Best Parameters: {'max_depth': 10, 'min_samples_leaf': 2, '...
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X and y variable definition for RF split of multiclass classification rating
I have the following variables: text (text of the review), rating (rating of the review 1 to 5), resp_text (managerial response to a review), month_before (binary variable that indicates whether or ...
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Which method is better to understand key drivers/feature importance in prediction?
After applying two different classifiers (EBM Classifier and Random Forest Classifier) and getting similar scores, I used InterpretML functionality to identify the most relevant features in each model....
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Struggling with normalization/Standardisation for machine learning dataset
Sorry for what is probably a very obvious/rookie question. I'm currently doing a data science module for my degree and making very slow progress with the work.
The case study i'm doing is around HR ...
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how can I fix my model to accurately assess zero values?
I'm building a random forest model that is designed to predict tree canopy cover. I'm using an R software environment and satellite remote sensing data as my predictors.
I'm okay with under or over ...
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How does hyperparameter tuning work for constructing/choosing a final model using Nested Cross validation?
I want to determine if XGBoost is better than random forest or logistic regression for building a binary classification model. The model will be a composite model, with a feature selection model to ...
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Strange situation RF classification: perfect train, test predictions all in a single category
I am puzzled by an issue with a boolean classification task using RF on a large dimensional dataset (1680 obs x 110 dim) and moderate imbalance (431 vs 1249).
train/test partition is random (0.8,0.2)
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Error while executing the adaptiverandomforest with river libraray
I have installed rivery library and write the adaptiverandomforestregression program to apply onlinelearning and I am getting errror that AttributeError: module 'river.ensemble' has no attribute '...
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How to apply online learnig to Random Forest?
I have developed a random forest model for wind veliocity prediction with hyperparameter tuning, but i am getting continuous data. So i want to apply online leearning for random forest model. could ...
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PR-AUC vs F1 vs Balanced Accuracy
I'm trying to create a Random Forest Classifier for selecting ~ 700 features.
I have a highly imbalanced dataset to select features from. There are significantly fewer positive cases (1%) compared ...
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sklearn Random Forest classifier vs R’s Random Forest classifier
I’m trying to implement the R’s random forest classifier equivalent in python-
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How to determine the optimal number of trees in Random Forest?
Here I list possible answers for mine:
Do you use the graph for OOB?
Do you use any other kind of graph?
Do you take a fixed number in default?
Do you take in consideration any research paper ...
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How do I know the appropriate number of iterations when using Miceforest for imputation?
I want to know how to avoid overfitting without having to increase the number of iterations excessively in Python with the Miceforest library. I know you can make a correlation map of data sets but I ...
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Random Forest Classifier Removing Features using Top-N Features Method
I am a new-comer to data science and machine learning techniques and processes. I'm working on a personal project that predicts the winner of an NBA game using a random forest classifier. I have ...
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Is a Random Forest Capable of Learning and Predicting Numerical Trends in Panel Data?
In a panel data set consisting of exponential functions, each indexed by an integer i ranging from 0 to 100. The exponential function is defined as f(i, t) = A(i) * e^(-r(i) * t), where A(i) is the ...
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Does it make sense to do hp tuning for a Random Forest for top k precision or recall?
I've trained an RF with a binary classification task that achieves mediocre performance. However, they way it is intended to be used would have end-users look only at predictions with high scores (...
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Feature importance using random forest vs. SHAP
I recently came across SHAP while looking for feature-importance methods. To use SHAP, first a model needs to be created, and then based on the predictions made by the model, SHAP values are ...
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I am a beginner in Knime and using Random Forest
Within Knime how am I able to identify which of the variables have the best predictive power within my model. I have successful ran the model and assessed model accuracy, recall and precision but can ...
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Why I'm getting a negative R2 score with Random Forest Regressor?
I'm trying to predict some variables for MOF's (from a scientific paper) using the Random Forest model in Phyton, but the value of R2 is negative (different from the paper, which was positive). I ...
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Model can not fit to 8 datapoints
I have 10 groups of biological experiments, all of size 100. I want to estimate experimental performance (success rate) of each groups of experiments, but have only ran experiments in two groups. My ...
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Target variable is discrete ranging from 1 to 14, with each value having same proportion in the dataset, ML models fail miserably
I have a dataset of shape (55314,23). The target variable is league_rank. There are exactly 3951 leagues in this dataset, with each club having a ranking from 1 to 14. The variable is discrete, and ...
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Random Forest overfitting to unbalanced data set
I am working on an unbalanced classification problem. I have have 2000 points which are positive, and 6000 points as -ve (chosen randomly from 100k universe of -ve points universe). Although I have ~...
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Can lag features be applied into test data without label?
can lag features be applied into test data without label? I've been wondering. I tried to build random forest model using dataset: training data (with label Y) and testing data (without label Y). The ...
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Validity of using raw time series data for training of xgboost/random forest classifier
I am currently working on a project aiming of classification of process states based on time series data. For this, we are looking at different models, such as XGBoost-based classifiers or ...
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Why do we need hyperparameter tuning in Scikit learn? Doesn't sk learn models by default give best model?
When I have the option to build a classifier like this directly
clf = RandomForestClassifier()
why do we perform tuning by restricting the parameters like this
<...
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ValueError: X has 54 features, but DecisionTreeClassifier is expecting 53 features as input
I am analysing and prediction 2023 Cricket World Cup based on previous given dataset.
This is Exploratary analysis:
Feature selection and Training model:
Applying Random forest classifier algorithm:
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Comparing random forest vs step wise regression
We have a dataset that we would like to compare the performance between random forest vs regression. However, due to the limited total amount of data, we used 5-fold cross validation with 5 repeats on ...
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How to know which rules were applied to predict one sample in trained decision tree model?
I have trained Random Forest Regressor from sklearn. I am able to return text representation from each Decision Tree rule using tree.export_text (sklearn documentation here). But it shows rules for ...
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How to Fix Dimension Issues of features and classes from a Multilabel Classification dataset in getting the Out-of-Bag Error of a Random Forest?
I have created a multilabel classification dataset using make_multilabel_classification from scikit learn:
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How to deal with "Could not broadcast input array from shape (1141,2) into shape (1141,)" to get Out-of-Bag error while using Random Forest
I have a dataset that consists of 171 features and 39 labels. I captured both features and labels of the dataset through slicing:
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Is probabilities mean of predicted class (RandomForest) a consistent estimator of class recall?
I'm working on a classification problem in order to predict among 50 different classes.
I'm using a Random Forest classifier and I'm using the predict_proba method ...
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Prediction in multiclass classification
Context: I need to make an multiclass classification to predict what type of sentence(law) the case will have in the end.
Data: I Have several columns to predict the case:client, cause of action, ...
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