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Questions tagged [feature-importances]

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Feature importance and classes importance

I have the following trained time series classification tensorflow model : ...
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Understanding most important features from an additional column

I'm fairly new to data science in general and I'm doing some analysis. Let us say I have N rows and D features, and I have a ...
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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 ...
Tamanna Mostafa's user avatar
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how to get feature importance on unseen test data

I trained a random forest classifier with a set of features and saved the model. (the features were selected based on their correlations with the response variable. Only those features with ...
Tamanna Mostafa's user avatar
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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....
Guilherme Atihe de Oliveira's user avatar
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Effect of feature selection when coupled with XGB models

I ran Boruta feature selection prior to XGB training\testing step and didn't see any difference, although ~30/200 features were rejected prior to going into the training. Can it be that internal ...
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Measuring features effect and importance in Partial Least Square (PLS) regression

Context: it is possible to assess features importance and effect for a model using model-independent scoring techniques such as Partial Dependence (PD) profile, Acculumated Local Effect (ALE) profile, ...
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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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3 answers
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Feature importance score for a feature that contains mostly 0's in XGBoost

I have read that the feature importance scores are calculated based on how a split on that feature improves performance. I have a binary classification dataset and am running XGBoost classifier on it. ...
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Feature Importance in Stacked Model

I have built a stacked model using mlxtend StakingCVClassifier. I want to know the feature importance scores now. Is there any way I can calculate feature importance scores for the stacked model? If ...
Anjali 's user avatar
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1 answer
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Why the marginal contribution of a feature is the difference between the feature effect minus the average effect

In several sources the marginal contribution is defined as the difference between the prediction with and without the feature. However, recently I read an article where the marginal contribution was ...
Amir Jalilifard's user avatar
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Feature importance in xgboost

I've been reading that feature importance in xgboost is computed the same way as in random forests. However, the learning rate reduces the effect of downstream trees. Is the learning rate taken into ...
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Shapley Values - How to interpret each value for each feature for a specific instance?

I am using Shap Values(the 'shap' module in python) to help me understand a bit better the relation between my features and my target. I am currently working on a binary classification problem. I know ...
Gabriel Monteiro's user avatar
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Capturing the 'direction' of feature importances using TreeSHAP?

I'm a machine learning / python novice, so apologies if my question is simple, but I haven't been able to find it addressed. I'm very interested in using ML to determine the most important features ...
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1 answer
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Decision tree vs logistic regression feature importances

I have trained Logistic regression and decision tree in skearn on the same standardized dataset (binary classification). Top important coefficients for the decision tree are (sorted by ...
Arseniy Maryin's user avatar
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What Would Be a Good Measure of Feature Importance in Regression?

Doing simple supervised regression where the label is a floating point number (guaranteed positive) and the features are a mix of continuous floating point values and some categorical features. What ...
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2 answers
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Deep-diving in Feature Importance

While calculating feature importance in random forest based on gini impurity(MDI offered by sklearn) or via correlation plots, few features with lesser amount of valid data fails to show it's real ...
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Feature importance with 0 score

I have feature importance score that showed like this picture. Why some of my features have 0 feature importance score (default, job_admin, education, job_enterpreneur, job_management, job_others, ...
Jovian Aditya's user avatar
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1 answer
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combine xgb feature importance

Is it possible to combine the results from the xgb.boost importance function. For example, due to one hot encoding, I have a feature age=35 and another age=60. Is there a way that I can add these to ...
Nikita Belooussov's user avatar
1 vote
2 answers
108 views

Global feature importance for classification problem where some values are irrelevant

I have a binary classification problem where most of the features are categorical with 4 possible values: yes, no, irrelevant, nan. I am trying to find the modular global feature importance of those ...
Ohm's user avatar
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2 votes
1 answer
601 views

Interpreting the variance of feature importance outputs with each random forest run using the same parameters

I noticed that I am getting different feature importance results with each random forest run even though they are using the same parameters. Now, I know that a random forest model takes observations ...
Detr4's user avatar
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2 answers
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How to interpret importance of random forest model, Mean Decrease Accuracy and Mean Decrease Gini?

A random forest model outputs the following importance values. How do I interpert them for feature selection? If it's the mean decreased accuracy does that mean that by removing them from the model ...
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Searching machine learning algorithm for regression problem with many features

I have a machine learning problem with about 160 features and 400 cases and I want to find the best predictors for a continuous outcome. The dataset contains variables of psychotherapists and clients. ...
Christopher Lalk's user avatar
1 vote
1 answer
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Is it possible for a feature not correlated with a dependent variable to become important in a machine learning model?

Is it possible for a feature not correlated (or faintly correlated) with a dependent variable to become important in a machine learning model?
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Differences between Feature Importance and SHAP variable importance graph

I have run an XGBClassifier using the following fields: ...
Giampaolo Levorato's user avatar
1 vote
1 answer
154 views

Understanding which variables impact your variable of interest the most (correlation, linear regression) and correctly interpreting results

How do you ascertain which variables lead to the greatest increase in another variable of interest? Let's say you have a correlation matrix. You look at the row of the variable you are particularly ...
Learning_and_xbox's user avatar
3 votes
2 answers
2k views

XGBoost model has features whose feature importance equal zero

I ran into this problem: A XGBoost model(.pickle file , constrcuted under V0.7.post3) with 100 features in it ; But I found 55 features in model (model.feature_importances_) show 0 feature importance ...
leveygao's user avatar
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1 answer
160 views

Why does an unimportant feature has a big impact on R2 in XGBoost? [closed]

I am training an XGBoost model, xgbr, using xgb.XGBRegressor() with 13 features and one numeric target. The R2 on the test set ...
volkan g's user avatar
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What kind of model to use to find drivers when data is aggregated and not on user level?

I have a website and have info from Google Analytics. So I can see the following "features": page url country device category (phone, desktop, etc.) Number of sessions Number of users: ...
user126224's user avatar
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Features importance in model

I've been using azure's auto ML platform for a couple of weeks now and recently I've trained a model and came across a strange looking aggregate feature importance chart in the explanations tab. The ...
Guilherme Takata's user avatar
3 votes
2 answers
7k views

difference between feature effect and feature importance

Is there a difference between feature effect (eg SHAP effect) and feature importance in machine learning terminologies?
xlra's user avatar
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2 answers
433 views

aggregation of feature importance

I have more of a conceptual question I was hoping to get some feedback on. I am trying to run a boosted regression ML model to identify a subset of important predictors for some clinical condition. ...
dean's user avatar
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2 answers
290 views

Concerns regarding small dataset with too many features

I have dataframe with 322 observations with 224 features. The observations has two classes, 0 or 1,which i'm trying to predict. class 0 has 168 observations and class 1 has 154 observations. I was ...
Reut's user avatar
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1 answer
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Does it make sense to use feature importances based in gini index for other classifiers?

I would like to know if makes sense running yellowbrick.features.FeatureImportances with a RandomForestClassifier model in order ...
heresthebuzz's user avatar
1 vote
1 answer
352 views

Shapley value, conditional expectation vs reference point

In Shapley, the marginal contribution of a feature is computed by comparing the performance of a model with and without a feature over all possible subsets of features. A common choice is using the ...
zzzbob's user avatar
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1 vote
1 answer
39 views

Revealing the causal structure in time-dependent data

We have a data table that accumulates the control and monitoring parameters of the High-Temperature Superconductor (HTS) production process: such that the rows represent the observations and columns ...
sorooshi's user avatar
1 vote
0 answers
75 views

How marginal contributions of adding a variable in a model is calculated in determining SHAP feature importance?

I was trying to find feature importance using SHAP values in python for Isolation Forest. SHAP calculates the feature importance of a feature($i$) pertaining to a model($f$) for a datapoint($x$) using ...
aishik roy chaudhury's user avatar
2 votes
1 answer
2k views

Feature importance with Text features

I would like to determine features importance in several models: support vector machine logistic regression Naive Bayes random forest I read that I will need an agnostic model, so I have thought to ...
Math's user avatar
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2 votes
2 answers
251 views

Node Importance in scikit learn

I'm trying to understand exactly how feature_importances in scikit-learn's RandomForestClassifier works. I managed to find this helpful link explaining most of the process: https://towardsdatascience....
J.D.'s user avatar
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2 answers
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Why is XGBClassifier in Python outputting different feature importance values with the same data across different repetitions?

I am fitting an XGBClassifier to a small dataset (32 subjects) and find that if I loop through the code 10 times the feature importances (gain) assigned to the features in the model varies slightly. I ...
user15733888's user avatar
1 vote
1 answer
162 views

Is feature importance from classification a good way to select features for clustering?

I have a large data set with many features (70). By doing preprocessing (removing features with too many missing values and those that are not correlated with the binary target variable) I have ...
ricecooker's user avatar
2 votes
1 answer
346 views

Feature Interactions vs Feature Importances

What are the differences between Feature Interactions and Feature Importances?
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