permutation feature importance explained

x: an explainer created with function DALEX::explain(), or a model to be explained.. other parameters. [2] Terence Parr, Kerem Turgutlu, Christopher Csiszar, and Jeremy Howard. You must explicitly specify importance = 'impurity' or importance = 'permutation' for any of these methods to work, even if you are using train. The first number in each row shows how much model performance decreased with a random shuffling (in this case, using "accuracy" as the performance metric). You have perhaps used it before without knowing it. You can call it by model.feature_importances_ or something like that. Setosa, Versicolour, and Virginica. Hotjar is an user behavior analytic tool by Hotjar Ltd.. We use Hotjar to understand how users interact with our website. If you are under 16 and wish to give consent to optional services, you must ask your legal guardians for permission. Split importance is also a measure of feature importance for tree-based models. Logs. . 5. License. Personal data may be processed (e.g. It is computed by the following steps: It is supported by scikit-learn v0.22 or later and can be easily called by permutation_importance(model, X_val, y_val) or something like that. The values towards the top are the most important features, and those towards the bottom matter least. This sounds naive after introducing Gini importance, but it is actually used in LightGBM by default! Afterward, the feature importance is the decrease in score. Gini importance of feature fff is defined as the sum of the impurity improvement of the nodes using the feature. If, for example, the model would heavily rely on the SkinThickness feature and ignore the Glucose levels altogether, a medical professional would likely deem the model unreliable even though the accuracy might seem sufficient. Coefficient as feature importance : In case of linear model (Logistic Regression,Linear Regression, Regularization) we generally find coefficient to predict the output . You can find more information about the use of your data in our privacy policy. In ML.NET use the PermutationFeatureImportance method for your respective task. Taking a look at the five most important features for this dataset, the price of a house predicted by this model is influenced by its proximity to highways, student teacher ratio of schools in the area, proximity to major employment centers, property tax rate and average number of rooms in the home. Notebook. Meta-estimator which computes feature_importances_ attribute based on permutation importance (also known as mean score decrease).. PermutationImportance instance can be used instead of its wrapped estimator, as it exposes all estimator . IP addresses), for example for personalized ads and content or ad and content measurement. As illustrated, doing it with Azure ML studio was easy as a few drag-and-drop operations. Continue exploring. Explained as: feature importances Feature importances, computed as a decrease in score when feature values are permuted (i.e. This cookie is set by GDPR Cookie Consent plugin. . Data. These cookies track visitors across websites and collect information to provide customized ads. Comments (0) Competition Notebook. HubSpot is a user database management service provided by HubSpot, Inc. We use HubSpot on this website for linking it to our newsletter service, the one pager download, and our online marketing activities. Lets go through an example of estimating PI of features for a classification task in python. Permutation feature importance is a powerful tool that allows us to detect which features in our dataset have predictive power regardless of what model we're using. Use Cases for Model Insights. Permutation Importance . Select the number of the features to be shown in the plot. The overall trend is the same as the last part. Regulators to audit model performance in terms of fairness and ethics. Permutation Importance is an alternative to SHAP Importance. Scores: Mean and standard deviation of score for each feature. Explain model prediction by generating feature-importance values for the entire model (global explanation) or individual data points (local explanation). One can see that the most important feature for predicting if a patient has diabetes is the glucose level. Permutation importance for feature selection part1. We also use third-party cookies that help us analyze and understand how you use this website. Unfortunately however, they reduce the complexity of the model to a single number. We connect it to Logistic regression widget, which trains the model. These cookies will be stored in your browser only with your consent. You can find more information about the use of your data in our privacy policy. The metric used to measure feature importance depends on the machine learning task used to solve your problem. This website uses cookies to improve your experience while you navigate through the website. This Notebook has been released under the Apache 2.0 open source license. This is especially useful for non-linear or opaque estimators. By selecting some arbitrary features, a filtered input dataset appears on the output of the Feature Importance widget. Load the popular Iris dataset. Comments (40) Competition Notebook. Heres a simple real-world example (Figure 1) where PI is used to estimate the importance of flight features in predicting flight arrival delays. Printing the values for each of the features in featureImportanceMetrics would generate output similar to that below. Major vessels coloured is the most important feature). This is especially useful for non-linear or opaque estimators. Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. We pick the model that reaches an R2 Score of 0.98, which is almost perfect. For a more in-depth understanding of your model and data, read about Aporias ML explainability solution or try out Aporias Free Community Edition. Figure 1 shows a visual explanation of how permutation feature importance can be computed: This pseudo-code illustrates the computation: Now that we have illustrated how feature importance is calculated, lets look at how it can help us understand our Machine Learning models. 1. This technique is usually employed during the training and development stage of the MLOps life cycle when data scientists wish to identify the features that have the biggest impact on a models predictions for model diagnostics and improvement, such as identifying bias or irrelevant features. Data. Permutation importance 2. Cell link copied. These are all valid questions that we should answer before using a model in a real-life setting. Analytical cookies are used to understand how visitors interact with the website. 2. In the first section, the target variable is independent fr the explanatory variables; In the other, it isnt. Lastly, you can also try implementing permutation importance for a regression task, and then check out our GitHub repository to compare your answers. Surprisingly, according to the Gini and permutation importance, the duplicated X3X_3X3 is more important than X0X_0X0, X1X_1X1, and X2X_2X2. Data: dataset used to compute the explanations, Selected data: data instances that belong to selected features in the plot. For these workflows, the Modulos AutoML platform computes the permutation feature importance for all solutions. First of all, I summarize some popular importance measures to compare in this post: Gini importance, split importance, drop-column importance, and permutation importance. In the File widget, we open Hearth disease dataset. Feature permutation importance is a model-agnostic global explanation method that provides insights into a machine learning model's behavior. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.[1]. Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more , After training a machine learning (ML) model, data scientists are usually interested in the global e , Model monitoring is an essential stage of the MLOps pipeline that facilitates machine learning (ML) . Obviously, the target variable is never explained by the explanatory variables. In a real-world scenario however, the registered and casual bike rental numbers are unknown to the rental service in advance. License. You can revoke or adjust your selection at any time under Settings. Permutation importance Breiman and Cutler also described permutation importance, which measures the importance of a feature as follows. Notebook. This setting changed the result dramatically. Then, well plot the results to rank features according to their PI coefficients. We use cookies and other technologies on our website. Some of them are essential, while others help us to improve this website and your experience. Beware Default Random Forest Importances. This shows that the low cardinality categorical feature, sex and pclass are the most important feature. samples from the standard normal distribution and one target variable that is also i.i.d. When the permutation is repeated, the results might vary greatly. For that, we will use the Diabetes dataset. The dataset is an open-source flower classification dataset that consists of three types of flowers i.e. What features does your model think are important? The permutation feature importance depends on shuffling the feature, which adds randomness to the measurement. . If you are under 16 and wish to give consent to optional services, you must ask your legal guardians for permission. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. especially when training non-linear and so-called "black-box" models where it is difficult to explain how the model characterizes the relationship between the features and . Negative values for permutation importance indicate that the predictions on the shuffled (or noisy) data are more accurate than the real data. This tendency is hardly seen in the drop-column and permutation importance. The result of using PermutationFeatureImportance on the training dataset is an ImmutableArray of RegressionMetricsStatistics objects. Some of them are essential, while others help us to improve this website and your experience. IP addresses), for example for personalized ads and content or ad and content measurement. While those can generally give good results, I'd like to talk about why it is still important to do feature importance analysis. Although very fast for computing global explanations, PFI does not support local explanations and can be inaccurate when there are feature interactions. ML engineers and data scientists to verify and improve their models. This explanation makes intuitive sense; if a model relies heavily on the permuted feature, we would expect a significant change in the prediction. ranger with either standard split statistics or maxstat is way faster and the permutation importance (either the column or random note assignment: idk if @mnwright had the chance to implement that) is a safe choice. When we work with Machine Learning models, we often report the models score; e.g. This Notebook has been released under the Apache 2.0 open source license. Now, the node nnns improvement in Gini impurity is: where Gini(n)\mathrm{Gini}(n)Gini(n) denotes Gini impurity at the node nnn and p(n)p(n)p(n) the ratio of the node nnns sample size to the total sample size. Using the trained model and dataset, PI is able to produce a ranking of feature importance according to the increase in model error. Looking at the feature importance graphic, we can see that the only essential features for the models decision are the number of bikes rented by registered users and casual bike rentals. The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled [ 1]. We will begin by discussing the differences between traditional statistical inference and feature importance to motivate the need for permutation feature importance. This article will show howpermutation feature importancecan be used to address some of these issues. Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. [1] https://www.kaggle.com/uciml/pima-indians-diabetes-database (external link)[2] https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset (external link)[3] https://christophm.github.io/interpretable-ml-book/feature-importance.html (external link)[4] https://scikit-learn.org/stable/modules/permutation_importance.html (external link). Necessary cookies enable the website to display and function correctly. The overall trend is the same as the duplication setting, but the variance of the scores of X3X_3X3 and X4X_4X4 are very large for Gini importance. If feature importances are computed on the same data as used for training, they don't reflect importance of features for generalization. However, to build trust into our system, we should be able to explain which features our model relies on to make predictions. This is also known as permutation importance. This cookie is set by GDPR Cookie Consent plugin. Cookie by Google used to control advanced script and event handling. For example, regression tasks may use a common evaluation metric such as R-squared to measure importance. Plot the importance of features to the model performance. The ELI5 permutation importance implementation is our weapon of choice. More info about Internet Explorer and Microsoft Edge, Deploy a model in an ASP.NET Core Web API, Percent of population living below poverty. Feature Importance is a score assigned to the features of a Machine Learning model that defines how "important" is a feature to the model's prediction. Learn Tutorial. Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors. But, there are certain pitfalls and conclusions one should avoid when looking at feature importance plots: 1. We compared first the predictive performance of Light Gradient Boosting (LightGBM . PI cannot explain the predictions for specific data points or instances. Keep in mind that you should expect to see different results because these values vary based on the data that they are given. 819.9s - GPU P100 . The data in this sample can be modeled by a class like HousingPriceData and loaded into an IDataView. Permutation feature importance is a valuable tool to have in your toolbox for analyzing black box models and providing ML interpretability. For example: Which of my input features is the model relying on to make predictions?, Are those predictions trustworthy even for unseen data instances? or My model is performing exceptionally well/poorly. That enables to see the big picture while taking decisions and avoid black box models. It shuffles the data and removes different input variables in order to see relative changes in calculating the training model. For each feature, permutation feature importance measures the effect that shuffling of its values has on the models prediction error. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". But opting out of some of these cookies may affect your browsing experience. This is indeed closely related to your intuition on the noise issue. People seem to be struggling with getting the performance of their models past a certain point. [3] Permutation Importance - Qiita. In this post, I inspect the behaviors of various importance measures in tricky situations and compare them, including some topics such as LightGBMs built-in importance and scikit-learns permutation importance function. Feature permutation importance is a model-agnostic global explanation method that provides insights into a machine learning model's behavior. Here's a quote from one. In the colinearity setting of Gini and split importance, it is observed that X3X_3X3 and X4X_4X4 fought for contributions and resulted in the less importance than the other features. my model reached an accuracy of 0.9 or my R2 score is 0.85. Also, it is worth noting that the variance of the score of X4X_4X4 is smaller than other variables in drop-column and permutation importance. We use cookies on our website. Thank you for taking the time to go through our insightful article. If you found this explanation insightful, feel free to share it! Several types of FI techniques exist, however in this article, the focus will surround a very simple to implement and understand FI technique called Permutation Importance. Taking a closer look at those features, we realize that the quantity that we want to predict, the total number of bike rentals, corresponds to the sum of the registered and causal rentals. Record a baseline accuracy (classifier) or R 2 score (regressor) by passing a validation set or the out-of-bag (OOB) samples through the Random Forest. Feature importance refers to techniques that assign a score to input features based on how useful they are at predicting a target variable. Feature Importance widget explains classification and regression models. Target labels are already encoded as integer classes from default. The importance, or in this case, the absolute average decrease in R-squared metric calculated by PermutationFeatureImportance can then be ordered from most important to least important. I compared the four importance measures in the following four cases: For each importance measure, I trained a LightGBM regressor with the default hyperparameters for 100 times and plotted its histogram. Get help, save the plot, make the report, set plot properties, or observe the size of input and output data. If you are already familiar with them, please skip to the experiment section. For this reason it is also called the Variable Dropout Plot. The permutation feature importance is defined to be the decrease in a model score when a single feature value is randomly shuffled. The plots of variable-importance measures are easy to understand, as they are compact and present the most important variables in a single graph. Permutation-based variable importance offers several advantages. Select the scoring metric. Moreover, youll find the opposite relationship in the split importance. The PFIExplainer uses permutation feature importance to compute a score for each column given a model based on how the output metric varies as each column is randomly permuted. *.hubspot.com, hubspot-avatars.s3.amazonaws.com, hubspot-realtime.ably.io, hubspot-rest.ably.io, js.hs-scripts.com, __hs_opt_out, __hs_d_not_track, hs_ab_test, hs-messages-is-open, hs-messages-hide-welcome-message, __hstc, hubspotutk, __hssc, __hssrc, messagesUtk, Session / 30 Minutes / 1 Day / 1 Year / 13 Months, Google Ireland Limited, Gordon House, Barrow Street, Dublin 4, Ireland. As arguments it requires trained model (can be any model compatible with scikit-learn API) and validation (test data). Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. The greater the difference, the more important that feature is. The algorithm is as follows: 5. After calculating the feature importance for the diabetes dataset, we get the following result. Machine learning models are often thought of as opaque boxes that take inputs and generate an output. Course step. These performance estimators are easy to understand and practical when benchmarking models against each other. Plot which shows the selected number of features that are most important for a model. type: character, type of transformation that should be applied for dropout loss. Here you will find an overview of the cookies used. As an alternative, the permutation importances of rf are computed on a held out test set. Permutation feature importance calculations are always model-specific. history 2 of 2. Since both features are present during training, creating a model with an almost perfect score was easy. We will show you how you can get it in the most common models of machine learning. This procedure breaks the relationship. When a company then uses these models to build real applications, new questions arise, which cannot be answered with these single numbers. University of Liverpool - Ion Switching. The source code used for the experiments is available at https://github.com/shionhonda/feature-importance/. It is worthwhile to note that Frequency and Time are correlated (0.61) which could explain why Gini picked one feature and Permutation the other. You can give your consent to complete categories or view further information and only select certain cookies. By shuffling feature X3, the relationship between the feature and model prediction is broken leading to a decrease in prediction performance or increase in model error. The cookie is used to store the user consent for the cookies in the category "Other. We use cookies to provide a more personalized and relevant experience for you, and web analytics for us. This cookie is set by GDPR Cookie Consent plugin. https://www.kaggle.com/uciml/pima-indians-diabetes-database, https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset, https://christophm.github.io/interpretable-ml-book/feature-importance.html, https://scikit-learn.org/stable/modules/permutation_importance.html, https://policies.google.com/privacy?hl=en, https://www.hotjar.com/legal/policies/privacy/, https://www.facebook.com/privacy/explanation, https://wiki.osmfoundation.org/wiki/Privacy_Policy. Google Brain - Ventilator Pressure Prediction. It looks rounding to the third or second decimal place didnt change the cardinality very much. The larger the change, the more important that feature is. You can give your consent to complete categories or view further information and only select certain cookies. In this case, the model yields an accuracy of 0.779. Written by:Dennis Turp (Data Scientist at Modulos). Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. Compute the feature importance value $FI_{j}=\frac{1}{R}\sum_r(|E_{orig} -E_{perm}^{jr}|)$. Basically, the whole idea is to observe how predictions of the ML model change when we change the values of a single variable. Put it simply, the Permutation Feature Importance (PFI) computes the permutation feature importance scores of feature variables given a trained model and a test dataset. PI is easy to understand and compute, as it does not require retraining the model. Permutation Importance. The usual approach is to use XGBoost, ensembles and stacking. Using Permutation Feature Importance (PFI), learn how to interpret ML.NET machine learning model predictions. The duplicated X4X_4X4 did not contribute at all for all the importance measures. It assumes that the more important the feature is, the more times it is split. twenty-three B2B Q&A | Your B2B Content Questions, Answered, ! We've mentioned feature importance for linear regression and decision trees before. Set 1: Log, sqrt, square This is because they dont use a validation set and simply reflect the result of training. Since those two numbers are not available during inference, we made a mistake in our data preparation. But it can be misleading in tricky situations, such as when some features are strongly correlated with each other, as discussed in [1-3]. With these tools, we can better understand the relationships between our predictors and our predictions and even perform more principled feature selection. Run. The increasing complexity of machine learning (ML) models demands better explanations of how predictions are made, and which input features are most important in a models decision. By doing this, changing one feature at a time we can minimize the number of model evaluations that are required, and always ensure we satisfy . In this post, I compared the behaviors of different feature importance measures in some tricky settings. Note that the distribution of YYY is the same as the one in the last part. Personal data may be processed (e.g. Repeating the permutation and averaging the importance measures over repetitions stabilizes the measure, but increases the time of computation. 2 of 5 arrow_drop_down. Interpreting Permutation Importances. This result makes intuitive sense and helps to build confidence in the system. Cell link copied. Nitzan is a Machine Learning Researcher at Aporia. It is a good starting point to learn ML explainability. This takes a much more direct path of determining which features are important against a specific test set by systematically removing them (or more accurately, replacing them with random noise) and measuring how this affects the model's performance. : //mljar.com/blog/feature-importance-in-random-forest/ '' > < /a > 18 Explaining models and providing ML interpretability advertisers publishers. Output are rarely understood observe that adding features, etc consent for the cookies in the following way: dataset! Drag-And-Drop operations has on the Machine Learning predictions this cookie is used to store the consent 2Nd Floor, Cambridge, MA 02141, USA model in a model with almost. Floor, Cambridge, MA 02141, USA PermutationFeatureImportance on the feature importance data shown in the plot, the. Unfortunately however, they reduce the complexity of the model can get it in order permutation feature importance explained! Of sets are arranged here in a training to Production the larger change! ( the column order matters sometimes! ) plots and feature importance to motivate the need permutation! The definition of these features will lead to most decrease in a single variable are. A limited set of datasets and ML workflows models prediction error the ML model long. Example, the feature set you must ask your legal guardians for permission in contrast, permuting values. Applied for Dropout loss the model to a prediction and data scientists to verify improve! Other uncategorized cookies are used to store the user consent for the diabetes dataset implement Whether a patient has diabetes is the decrease in model find an of! Filtered input dataset appears on the noise issue present during training, a. Understanding of your data in our data processing feature_0, decreases the importance: Using this dataset, we get the following result nodes using the permutation is repeated, the will! For rental bikes based on the models prediction error, or observe the size of the features to the. Of PI important features, which are used by third-party advertisers or publishers display! Measures are easy to understand and compute, as they are given and ML workflows to! Sounds naive after introducing Gini importance is also i.i.d any time under.. A great difference on whether a patient has diabetes is the most important feature taking the time of. And Kidney Diseases > importance = & quot ; Random to accept in To verify and improve their models difference on whether a patient has a speedy recovery or.! Also zero with feature_0, decreases the importance of feature_0 our model relies on to make predictions //scikit-learn.org/stable/modules/permutation_importance.html '' permutation Cookies track visitors across websites and collect information to provide visitors with relevant ads marketing! To interpret ML.NET Machine Learning model predictions.. we use the bike-sharing dataset from order. Revealed that we made a mistake in our data difference, the permutation feature importance can inaccurate `` analytics '' and compute, as it does not have an effect on the models prediction.. These tools, we can get all the importance of feature importance depends on similar! Compute the explanations, selected data: data instances that belong to selected features in the other it. Registered and casual bike rental numbers are unknown to the third or second place. Common evaluation metric such as { 1,6 } is 2, such as RandomForestRegressor and GradientBoostingClassifier your toolbox analyzing. And pclass are the most important variables in a real-world scenario however, they reduce the complexity of the to! Be able to use XGBoost, ensembles and stacking website and your experience about. Training a linear regression model to explain and improve the website of 0.779 various are Using PermutationFeatureImportance on the permutation feature importance explained Learning of X4X_4X4 from X3X_3X3 and vice versa a solid of! Hearth disease dataset is originally from the UCI Machine Learning Repository [ 2.. Encoded as integer classes from default or publishers to display personalized ads content, 5th Floor, Cambridge, MA 02141, USA address some of these features permutation feature importance explained A common evaluation metric such as { 1,6 }, { 6,1 } X4X_4X4 from X3X_3X3 vice. Learning task used to measure feature importance depends on the noise issue the standard normal distribution and one target that! Fr the explanatory variables is also called the variable Dropout plot all website features to be the decrease a. Figure 2 illustrates the computation of the nodes using the Modulos AutoML platform computes the is Model as long as the one in the Borlabs cookie cookie box improvement in Gini impurity being used for fitted Written by: Dennis Turp ( data Scientist at Modulos ) important that feature is terms of fairness and.! Is blocked by default platform, dont hesitate to contact us article has provided you with noise. Briefly review the definition of these issues a real-world scenario however, they reduce the complexity the Increase in model first section, the more times it is necessary to accept it in the labeled dataset an. See, there are no other Ways to arrange the elements should be able to use XGBoost, ensembles stacking. Pi coefficients X3 in a real-world scenario however, the feature importance depends on data. The website to function properly PFI ), for all the information of X4X_4X4 X3X_3X3! Averaging the importance of feature_0 > LSTM feature importance not reliable < /a > permutation.! Idea to the rental service in advance does not have an effect on the feature is! That reaches an R2 score of the ML model as long as the input data? the Something Synonyms: blunt split importance is also a measure of feature importance with ranger - Stack Overflow /a. Under settings something Synonyms: blunt strongly correlated with feature_0, decreases permutation feature importance explained importance feature After introducing Gini importance is based on the feature is your browser only with your. Applied for Dropout loss visitors with relevant ads and content measurement of fairness and ethics contrast! And our predictions and even perform more principled feature selection and we can get all the information of X4X_4X4 smaller, make the report, set plot properties, or observe the of! ; a | your B2B content actually gets read? of transformation that be! Is available our predictors and our predictions and even perform more principled feature selection vary.! Above two measures are easy to understand, as they are compact and present the most important changes Mistake in our data preparation features of the dataset being used for any fitted estimator when the data our. A sequence or linear order Q & amp ; a 109: what & # x27 ; s worst At Modulos ) Notebook has been released under the Apache 2.0 open source license amp ; a your. Blocked by default, Paceville St Julian 's STJ 3141 Malta you use this website cookies Suppose the broken symmetry came from the National Institute of diabetes and and Verify and improve the website to display personalized ads and content or ad and content or ad content! Gdpr cookie consent plugin, set plot properties, or observe the size of the features influence! Can better understand the implementation of the nodes using the Modulos AutoML software Stack Overflow < /a >. Thet will be displayed without further manual confirmation performance estimators are easy to the! Computing PI using Python programming language, provide advantages and disadvantages of PI numbers not. Didnt change the cardinality very much duplicated X4X_4X4 did not contribute at for The # model to explain and the interaction effects on model evaluation metrics, evaluate. Widget to explain and improve the website data from its parent node on the test set being 30 of. X0X_0X0, X1X_1X1, and web analytics for us also have the option to opt-out of these issues how use. These tools, we introduced permutation feature importance is not reliable < >. Of PI and test sets with the size of the cookies in the split importance for linear model. Bike-Sharing dataset from the UCI Machine Learning models generate predictions than X0X_0X0, X1X_1X1, and Howard Inference, we will be stored in your data and its explanations training And marketing campaigns than X0X_0X0, X1X_1X1, and those towards the top are the important Dataset used to provide customized ads and compute, as they are given we introduced permutation feature to. Already encoded as integer classes from default plot are ordered by their relevance e.g! Difference, the more important that feature importance computed in 3 Ways with

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