Questions tagged [feature-importance]

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Does accuracy increase linearly when you add more factors/features to a random forest?

I have read in various machine learning books that adding more data should result in a more accurate model. Is this rule the same for adding more factors/features to the model as well? So, should I ...
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1answer
28 views

Feature Importance for Multinomial Logistic Regression

I have trained a logistic regression model with 4 possible output labels. I want to determine the overall feature importance for each feature irrespective of a specific output label. In case of binary ...
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0answers
15 views

Random forest visualization Python [duplicate]

For a dataset I ran a random forest model with sikit learn. When I checked important variables from RF , I was unable to understand the reason for choosing that variable as important. In my ...
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13 views

Mean Decrease Accuracy and Mean Decrease GINI in Random Forests

I performed a pixel based classification with random forest method and I have as output these 2 plots named: "Mean Decrease Accuracy" and "Mean Decrease GINI'. I googled around on how to describe my ...
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11 views

How to select a feature selection method?

Is there any other way to evaluate feature selection quality than model performance? I worry that if my model is overfitting this will then bias which selection of features seems to be best. I use ...
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14 views

what is the difference between feature importance and global explanation of the model?

Feature importance means how much that particular feature supported to derive that particular prediction.global explanation means explains how the model behaves generally. but i need clear explanation ...
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1answer
24 views

Feature Importance for Each Observation XGBoost

So I know there is a feature_importances_ variable under the XGBoost classifier. I was wondering if there is a way to see the deciding features for each observation? This will allow me to understand ...
2
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1answer
74 views

Is feature importance in Random Forest useless?

For Random Forests or XGBoost I understand how feature importance is calculated for example using the information gain or decrease in impurity. In particular in sklearn (and also in other ...
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2answers
54 views

How to find important features in unsupervised data?

I have data that defines the characteristics of an elevator. This data contains hundred of fields (height, weight, speed, number of persons inside the elevator, etc...). From this data, I want to ...
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2answers
36 views

Unbiased Measurement of Feature Importance in

My questions are about some of the details in the appendix of the paper Unbiased Measurement of Feature Importance in Tree-Based Methods (on arXiv). On p.13, it says: Given that the test data is ...
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0answers
30 views

Random Forests Variable Importance feature addition

I would like to know if the following is a valid method in concern to the final feature importance calculation: I have a number of training data with a total of e.g. 5 original features (OF). I ...
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0answers
9 views

Understanding the influence of multiple dimensions on a binary outcome - what method to use?

I've got a large quantity of multi-variate data, including 10 dimensions along with a binary outcome. I want to understand each of the dimension's influence on the binary outcome (ideally ranked) and ...
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1answer
39 views

random forest feature selection

For what I know RF can be used as a model for feature importance or feature selection. Also, that RF can be used as a prediction and classification model. The question that I have is if its possible ...
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1answer
20 views

Identifying Feature Importance in Text

I am trying to perform feature interpretability on a text corpus but I am becoming quite confused as to how I identify the importance of particular features (words). I have done substantial research ...
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0answers
10 views

Variable Importance sorting by absolute value of x or fully standardized coefficient?

I am looking at the output of a linear regression model and would like to sort the IVs by feature importance. In this case I want to use the absolute value of the standardized coefficients since my ...
2
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1answer
79 views

Finding the optimal split threshold for a feature using XGBoost in R

While implementing a gradient-boosted tree algorithm on a dataset, is it possible to learn the optimal value for a feature that best predicts a class? For example: In the iris dataset, what is the ...
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17 views

Reduction in number of observation by extracting piecemeal signal features,while keeping the no of features same. Can it be called feature extraction?

I have a dataset generated from 9 sensors in an E-nose system for a binary class classification problem. The system provides a response for 240 seconds for each sample. i.e. I have a data set of 240 * ...
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8 views

Feature importance with nested cross-validation - iml

I am using nested cross-validation to get performance estimates in a classification task. I would like to compute feature importance using permutations with the iml ...
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2answers
73 views

Random Forest in R - How to perform feature extraction and reach the best Accuracy result?

I'm working on a university project where I need to build a Random Forest model in R to predict if patients have depressive tendencies according to their EEG-data. I already preprocessed the data and ...
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1answer
28 views

Selecting Feature weights

I use the knn Classifier for a binary classification problem. To improve the classification results I would like to multiply features by weights that are learned from data. I found different ways to ...
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0answers
26 views

Best Feature selection algorithm Boruta, Step, Information Values(WoE) or RFE

I have landing data with 103 columns Would like to understand which algorithm tis best for feature selection and what may be the logic to call any feature as best. I have landing data with 103 ...
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1answer
22 views

How to compare predictive accuracy of various predictors

In my dataset, I have one dependent variable and 6 explanatory variables and I am interested in the question which predictor is the best. The relationship between the explanatory variables and the ...
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1answer
26 views

How to find significant predictors that can differentiate case and control without ML approach?

I have a dataset with more than 70 columns and I have an binary output column. What I did currently was to explore the dataset by plotting the bar and line graphs for the input variables vs output ...
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15 views

does the resolution of the vectors influence the result?

I have setup a model for prediction of free parking places. Thi model based on SVR (support vector regression) and sklearn libs. My feature vectors are: time as float (e.g. 8.00, 8.25, 8.5, 8.75, 9.0....
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0answers
128 views

feature importance for SVM with a nonlinear kernel

Using sklearn, I did SVR using rbf kernel. Though I got good results, problem is I don't know how to get the important feature that the algorithm used. Also coef_ ...
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0answers
68 views

Feature extraction (LDA) on one hot encoded variables

Background I am building a machine learning model which identifies variable importance associated with a binary classification problem for guiding data aggregation that a human can then act on. In ...
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0answers
7 views

method for determing the important factor for high dimension categorical data

I have around 1000 people with total 400 categorical features, but each one will only have subset of those 400 features(ranging from 3-60 for this population), thus the dataset is fairly sparse. Now I ...
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1answer
38 views

Model evaluation for feature selection

I have a dataset of gene expression data and I'm trying to find genes related to particular diseases. My labels are dichotomous (sick - not sick) and I used a Logistic regression with LASSO ...