Questions tagged [feature-importances]
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Rules/Guidelines for Custom Weightage and Hyper-parameter tuning
I have a movie and user-ratings dataset. After implementing the content-based filtering technique, I figured, I can improvise the results even further by assigning weightage to the parameters based on ...
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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 ...
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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 ...
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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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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 decision tree are (sorted by tree....
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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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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, ...
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How to select the best 30 features from 500 features for sales prediction model where feature importance can change over time?
I'm using data sets for sales prediction model which is trained every 2 weeks. It
has 200 features and 500 rows. I have to select the best 30 features that can be used in the
model instead of 200 ...
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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 ...
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How to calculate the feature importance of variables synthesized with EFB in LightGBM?
I've been studying LightGBM recently.
I have a question. Feature Importance can be extracted when running the LightGBM library in Python.
LightGBM has an Exclusive feature bundling feature that allows ...
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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 ...
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RandomForestRegressor.feature_importances_ applied on classification problem
friends!
I am new to the machine learning field, but so motivated to study more. I am currently conducting a study on the Wisconsin Breast Cancer Dataset and I apply some ML algorithms to study their ...
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Feature importance of a linear regression
What is the easiest and easy to explain feature importance calculation for linear regression?
I know I can use Shap to compute feature importance, but I find it difficult to explain it to stakeholders,...
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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 ...
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How to get significativity for tabular data in machine learning?
Im using fastai to train a network on tabular data (https://docs.fast.ai/tutorial.tabular.html).
I have a table with 5 columns, each of these is the specific attribute that describes a galaxy and ...
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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. ...
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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:
...
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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 ...
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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
...
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Why does an unimportant feature has a big impact on R2 in XGBoost?
I am training an XGBoost model, xgbr, using xgb.XGBRegressor() with 13 features and one numeric target. The R2 on the test set ...
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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: ...
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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 ...
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difference between feature effect and feature importance
Is there a difference between feature effect (eg SHAP effect) and feature importance in machine learning terminologies?
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235
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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....
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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 ...
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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 ...
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Feature Interactions vs Feature Importances
What are the differences between Feature Interactions and Feature Importances?
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