Trending December 2023 # Google On Effect Of Low Quality Pages On Sitewide Rankings # Suggested January 2024 # Top 12 Popular

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In a Google Webmaster Hangout, someone asked if poor quality pages of a site could drag down the rankings of the entire site. Google’s John Mueller’s answer gave insights into how Google judges and ranks web pages and sites.

Do a Few Pages Drag Down the Entire Site?

The question asked if a section of a site could drag down the rest of the site.

The question:

“I’m curious if content is judged on a page level per the keyword or the site as a whole. Only a sub-section of the site is buying guides and they’re all under their specific URL structure.

Would Google penalize everything under that URL holistically? Do a few bad apples drag down the average?”

Difference Between Not Ranking and Penalization

John Mueller started off by correcting a perception about getting penalized that was inherent in the question. Web publishers sometimes complain about being penalized when in fact they are not. What’s happening is that their page is not ranking.

There is a difference between Google looking at your page and deciding not to rank it.

When a page fails to rank, it’s generally because the content is not good enough (a quality issue) or the content is not relevant to the search query (relevance being to the user). That’s a failure to rank, not a penalization.

A common example is the so-called Duplicate Content Penalty. There is no such penalty. It’s an inability to rank caused by content quality.

Another example is the Content Cannibalization Penalty, which is another so-called penalty that is not a penalty.

A penalty is something completely different in that it is a result of a blatant violation of Google’s guidelines.

John Mueller Defines a Penalty

Google’s Mueller began his answer by first defining what a penalty is:

“Usually the word penalty is associated with manual actions. And if there were a manual action, like if someone manually looked at your website and said this is not a good website then you would have a notification in Search console.

So I suspect that’s not the case…”

How Google Defines Page-Level Quality

Google’s John Mueller appeared to say that Google tries to focus on page quality instead of overall site quality, when it comes to ranking. But he also said this isn’t possible with every website.

Here is what John said:

“In general when it comes to quality of a website we try to be as fine grained as possible to figure out which specific pages or parts of the website are seen as being really good and which parts are kind of maybe not so good.

And depending on the website, sometimes that’s possible. Sometimes that’s not possible. We just have to look at everything overall.”

Why Do Some Sites Get Away with Low Quality Pages?

I suspect, and this is just a guess, that it may be a matter of the density of the low quality noise within the site.

For example, a site might be comprised of high quality web pages but feature a section that contains thin content. In that case, because the thin content is just a single section, it might not interfere with the ability of the pages on the rest of the site from ranking.

In a different scenario, if a site mostly contains low quality web pages, the good quality pages may have a hard time gaining traction through internal linking and the flow of PageRank through the site. The low quality pages could theoretically hinder a high quality page’s ability to acquire the signals necessary for Google to understand the page.

Here is where John described a site that may be unable to rank a high quality page because Google couldn’t get past all the low quality signals.

Here’s what John said:

“So it might be that we found a part of your website where we say we’re not so sure about the quality of this part of the website because there’s some really good stuff here. But there’s also some really shady or iffy stuff here as well… and we don’t know like how we should treat things over all. That might be the case.”

Effect of Low Quality Signals Sitewide

John Mueller offered an interesting insight into how low quality on-page signals could interfere with the ability of high quality pages to rank. Of equal interest he also suggested that in some cases the negative signals might not interfere with the ability of high quality pages to rank.

So if I were to put an idea from this exchange and put it in a bag to take away with me, I’d select the idea that a site with mostly low quality content is going to have a harder time trying to rank a high quality page.

And similarly, a site with mostly high quality content is going to be able to rise above some low quality content that might be separated into it’s own little section. It is of course a good idea to minimize low quality signals as much as you can.

Watch the Webmaster Hangout here.

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Data Leakage And Its Effect On The Performance Of An Ml Model

This article was published as a part of the Data Science Blogathon


Let’s start our discussion by imagine a scenario where you have tested your machine learning model well, and you get absolutely perfect accuracy. After getting the accuracy, you are happy with your work and say well done to yourself, and then decide to deploy your project. However, when the actual data is applied to this model in the production, you get poor results. So, you think that why did this happen and how to fix it?

The possible reason for this occurrence is Data Leakage. It is one of the leading machine learning errors. Data leakage in machine learning happens when the data that we are used to training a machine learning algorithm is having the information which the model is trying to predict, this results in unreliable and bad prediction outcomes after model deployment.

                                                Image Source: Link

So, In this article, we will discuss all the things related to Data Leakage including what it is, how it has happened, how to fix it, etc. So, If you are a Data Science enthusiast, then read this article completely since it is one of the most important concepts that you as a Data Science enthusiast must know to accelerate your Data Science Journey.

Table of Contents

The topics which we are going to discuss in this detailed article on Data Leakage are as follows:

What is meant by Data Leakage?

How does Data Leakage exactly happen?

What are the examples of Data Leakage?

How to detect Data Leakage?

How to Fix the Problem of Data Leakage?

What is meant by Data Leakage?

Data Leakage is the scenario where the Machine Learning Model is already aware of some part of test data after chúng tôi causes the problem of overfitting.

In Machine learning, Data Leakage refers to a mistake that is made by the creator of a machine learning model in which they accidentally share the information between the test and training data sets. Typically, when splitting a data set into testing and training sets, the goal is to ensure that no data is shared between these two sets. Ideally, there is no intersection between these two sets. This is because the purpose of the testing set is to simulate the real-world data which is unseen to that model. However, when evaluating a model, we do have full access to both our train and test sets, so it is our duty to ensure that there is no overlapping between the training data and the testing data (i.e, no intersection).

How does it exactly happen?

Let’s discuss the happening of data leakage problem in a much detailed manner:

When you split your data into training and testing subsets, some of your data present in the test set is also copied in the train set and vice-versa.

As a result of which when you train your model with this type of split it will give really good results on the train and test set i.e, both training and testing accuracy should be high.

But when you deploy your model into production it will not perform well, because when a new type of data comes in it won’t be able to handle it.

Examples of Data Leakage

In this section, we will discuss some of the example scenarios where the problem of data leakage occurs. After understanding these examples, you have better clarity about the problem of Data Leakage.

General Examples of Data Leakage

To understand this example, firstly we have to understand the difference between “Target Variable” and “Features” in Machine learning.

Target variable: The Output which the model is trying to predict.

Features: The data used by the model to predict the target variable.

Example 1-

The most obvious and easy-to-understand cause of data leakage is to include the target variable as a feature. What happens is that after including the target variable as a feature, our purpose of prediction got destroyed. This is likely to be done by mistake but while modelling any ML model, you have to make sure that the target variable is differentiated from the set of features.

Example 2 –

To properly evaluate a particular machine learning model, we split our available data into training and test subsets. Invariably, what happens is that some of the information from the test set is shared with the train set, and vice-versa. So, another common cause of data leakage is to include test data with training data. Therefore, It becomes necessary to test the models with new and previously unseen data. If we include the test data in the training, then the process would defeat this purpose.

In real-life problem statements, the above two cases are not very likely to occur because they can easily be spotted while doing the modelling. So, now let’s see some more dangerous causes of data leakage that can sneak.

Presence of Giveaway features

Giveaway features are those features from the set of all features that expose the information about the target variable and would not be available after the model is deployed.

Let’s consider this with the help of the following examples:

Example 1 – 

Let’s we are working on a problem statement in which we have to build a model that predicts a certain medical condition. If we have a feature that indicates whether a patient had a surgery related to that medical condition, then it causes data leakage and we should never be included that as a feature in the training data. The indication of surgery is highly predictive of the medical condition and would probably not be available in all cases. If we already know that a patient had a surgery related to a medical condition, then we may not even require a predictive model to start with.

Example 2 – 

Let’s we are working on a problem statement in which we have to build a model that predicts if a user will stay on a website. Including features that expose the information about future visits will cause the problem of data leakage. So, we have to use only features about the current session because information about the future sessions is not generally available after we deployed our model.

Leakage during Data preprocessing

While solving a Machine learning problem statement, firstly we do the data cleaning and preprocessing which involves the following steps:

Evaluating the parameters for normalizing or rescaling features

Finding the minimum and maximum values of a particular feature

Normalize the particular feature in our dataset

Removing the outliers

Fill or completely remove the missing data in our dataset

The above-described steps should be done using only the training set. If we use the entire dataset to perform these operations, data leakage may occur. Applying preprocessing techniques to the entire dataset will cause the model to learn not only the training set but also the test set. As we all know that the test set should be new and previously unseen for any model.

How to detect Data Leakage?

Let’s consider the following three cases to detecting data leakage:

Case-1: Case-2: 

While doing the Exploratory Data Analysis (EDA), we may detect features that are very highly correlated with the target variable. Of course, some features are more correlated than others but a surprisingly high correlation needs to be checked and handled carefully. We should pay close attention to those features. So, with the help of EDA, we can examine the raw data through statistical and visualization tools.


After the completion of the model training, if features are having very high weights, then we should pay close attention. Those features might be leaky.

How to fix the problem of Data Leakage?

The main culprit behind this is the way we split our dataset and when. The following steps can prove to be very crucial in preventing data leakage:

Idea-1 (Extracting the appropriate set of Features)

Figure showing the selection of best set of features for your ML Model

                                                        Image Source: Link

To fix the problem of data leakage, the first method we can try is to extract the appropriate set of features for a machine learning model. While choosing features, we should make sure that the given features are not correlated with the given target variable, as well as that they do not contain information about the target variable, which is not naturally available at the time of prediction.

Idea-2 (Create a Separate Validation Set)

Figure Showing splitting of the dataset into train, validation, and test subsets

                                                         Image Source: Link

To minimize or avoid the problem of data leakage, we should try to set aside a validation set in addition to training and test sets if possible. The purpose of the validation set is to mimic the real-life scenario and can be used as a final step. By doing this type of activity, we will identify if there is any possible case of overfitting which in turn can act as a caution warning against deploying models that are expected to underperform in the production environment.

Idea-3 (Apply Data preprocessing Separately to both Train and Test subsets)

Figure Showing How a Data can be divided into train and test subsets

                                                         Image Source: Link

While dealing with neural networks, it is a common practice that we normalize our input data firstly before feeding it into the model. Generally, data normalization is done by dividing the data by its mean value. More often than not, this normalization is applied to the overall data set, which influences the training set from the information of the test set and eventually it results in data leakage. Hence, to avoid data leakage, we have to apply any normalization technique separately to both training and test subsets.

Idea-4 (Time-Series Data)

Figure Showing an example of Time-Series Data

                                                            Image Source: Link

Problem with the Time-Series Type of data:

When dealing with time-series data, we should pay more attention to data leakage. For example, if we somehow use data from the future when doing computations for current features or predictions, it is highly likely to end up with a leaked model. It generally happens when the data is randomly split into train and test subsets. 

So, when working with time-series data, we put a cutoff value on time which might be very useful, as it prevents us from getting any information after the time of prediction.

Idea-5 (Cross-Validation)

Figure Showing Idea Behind Cross-Validation

                                                          Image Source: Link

When we have a limited amount of data to train our Machine learning algorithm, then it is a good practice to use cross-validation in the training process. What Cross-validation is doing is that it splits our complete data into k folds and iterates over the entire dataset in k number of times and each time we are using k-1 fold for training and 1 fold for testing our model.

To know more about Cross-Validation and its types, you can refer to the following article:

Detailed Discussion on Cross-Validation and its types


So, as a concluding step we can say that Data leakage is a widespread issue in the domain of predictive analytics. We train our different machine learning models with known data and expect the model to perform better predictions or results on previously unseen data in our production environment, which is our final aim. So, for a model to have a good performance in those predictions, it must generalize well. But Data leakage prevents a model to generalize well and thus causes some false assumptions about the model performance. Therefore, to create a robust and generalized predictive model, we should pay close attention to detect and avoid data leakage. This ends our today’s discussion on Data Leakage!

Congratulations on learning the most important concept of Machine Learning which you must know while working on real-life problems related to Data Science! 👏

Other Blog Posts by Me

You can also check my previous blog posts.

Previous Data Science Blog posts.


Here is my Linkedin profile in case you want to connect with me. I’ll be happy to be connected with you.


For any queries, you can mail me on Gmail.

End Notes

Thanks for reading!

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Google Warns Of Low Realtime Data In Universal Analytics Reports

Google warns the Real-Time report in Google Analytics may be displaying inaccurate data for Universal Analytics properties.

If your Google Analytics account is impacted by this issue, realtime data for your Universal Analytics properties will appear lower than it actually is.

The only way to be sure your realtime data is accurate is to migrate to Google Analytics 4 (GA4), if you haven’t already.

Google published the warning about inaccurate data at the top of a Help Center article, which was spotted by Charles Farina.

Google has added a warning for the real-time analytics reports in Universal Analytics. There are ongoing issues, which cause them to be unreliable.

— Charles Farina (@CharlesFarina) April 14, 2023

“You may notice low realtime data in the Real-Time report in your Universal Analytics property. To get the most accurate realtime data, it’s recommended that you use the Realtime report in a Google Analytics 4 property.”

What Does This Mean For My Website?

Any unusual drops in your realtime metrics as of late can potentially be attributed to this issue affecting Universal Analytics properties.

Again, the only way to know for sure is to check the realtime report in a GA4 property.

Since the issue affects Universal Analytics properties only, it’s unlikely Google will make fixing it a priority.

Google is dropping support for Universal Analytics properties on July 1, 2023, , at which time GA4 is going to be the new standard.

Is The Data Lost?

The data missing from the real-time report isn’t gone for good.

Although the hits weren’t tracked in the real-time report, they were still recorded and attributed correctly in other reports.

The Real-Time report allows you to monitor activity on your site as it happens. Data is reported in seconds, which means it’s constantly changing.

There are specific use cases where the report is indispensable.

You can see, for example, how well an article is doing after being shared on Twitter for the first time.

Or you can see whether a limited time promotion is driving traffic to your site as intended.

That’s how it’s supposed to work, anyway.

With Universal Analytics properties in their current state, data is your Real-Time report could now be artificially low.

However, if realtime data isn’t important to you, then this issue won’t disrupt your use of Google Analytics.

If you rely on this report to monitor website performance throughout the day, and haven’t yet migrated to GA4, now is a good time to consider it.

Source: Google Analytics Help

Featured Image: DC Studio/Shutterstock

The Effect On The Coefficients In The Logistic Regression

Statistically, the connection between a binary dependent variable and one or more independent variables may be modeled using logistic regression. It is frequently used in classification tasks in machine learning and data science applications, where the objective is to predict the class of a new observation based on its attributes. The coefficients linked to each independent variable in logistic regression are extremely important in deciding the model’s result. In this blog article, we’ll look at the logistic regression coefficients and how they affect the model’s overall effectiveness.

Understanding the Logistic Regression Coefficients

It is crucial to comprehend what the logistic regression coefficients stand for before delving into their impact. To measure the link between each independent variable and the dependent variable, logistic regression uses coefficients. When all other variables are held constant, they show how the dependent variable’s log odds change as the corresponding independent variable increases by one unit. The logistic regression equation has the following mathematical form −

$$mathrm{log(p/1-p) = β0 + β1X1 + β2X2 + … + βnXn}$$

where the intercept is 0 and the coefficients for each independent variable (X1 to Xn) are 1 to n, and p is the probability of the dependent variable (usually shown as 0 or 1).

Effect of the Coefficients on Logistic Regression

In logistic regression, the coefficients are critical in deciding the model’s result. The logistic curve’s form, in turn, impacts the anticipated probability, depending on the size and sign of the coefficients. Let’s look more closely at how the coefficients affect the logistic regression model.

1. Magnitude of Coefficient

The magnitude of the coefficients in logistic regression indicates how closely the independent and dependent variables are connected. With a larger coefficient, the correlation between the independent and dependent variables is stronger. On the other hand, when the coefficient is lower, the link between the independent and dependent variables is weaker. Or, to put it another way, a little change in an independent variable with a large coefficient can have a tremendous impact on the predicted likelihood.

2. Sign of the Coefficients

The direction of the link between the independent and dependent variables in logistic regression is shown by the sign of the coefficients. An increasing independent variable enhances the chance of the dependent variable, which is shown by a positive coefficient. As the independent, variable rises, the likelihood of the dependent variable falls, which is shown by a negative coefficient.

3. Interpretation of the Coefficients

With logistic regression, the coefficients must be interpreted considerably differently than for linear regression. As the independent variable grows by one unit, the dependent variable also changes, as seen by the coefficients in linear regression. The log odds of the dependent variable change in contrast to an increase of one unit in the independent variable, according to the logistic regression coefficients. Understanding how the coefficients impact the model’s predictions is important, even though this interpretation could be a bit difficult.


In logistic regression, the coefficients are critical in deciding the model’s result. They aid in determining the anticipated probability and quantifying the link between the independent and dependent variables. The performance and predicted accuracy of the logistic regression model can be enhanced by comprehending the impact of the coefficients. In conclusion, it is crucial to carefully analyze the significance of the size and sign of the coefficients in logistic regression in order to create a successful model.

What Causes Google Rankings To Be Highly Volatile?

Editor’s note: “Ask an SEO” is a weekly column by technical SEO expert Jenny Halasz. Come up with your hardest SEO question and fill out our form. You might see your answer in the next #AskanSEO post! 

Welcome to another edition of Ask an SEO! Today’s question comes from Dwight in Texas. He asks:

Why is it that we see lately our rank on Google for “auto shipping” go from 6 to unranked? Then, in a day or so it shows back up in or around the same rank. We have not done anything to the site that should cause this volatility. We have an SEO service working monthly on the site. Our SEO score is pretty good on RYTE and Moz. We have lost most of our SERP on the high volume keywords over the past 2 years. We can’t seem to recoup our rankings and what we have is highly volatile… Any ideas? Thanks!

Google has been highly volatile lately. There have been a series of updates that have left a lot of sites scrambling.

Depending on the industry, a few of the sites I monitor also saw volatility.

I’m not going to go into details on what we think was in the algorithms themselves (i.e., what types of sites were winners and losers) because others have already done a great job of that.

I’m also not an algo chaser, so I generally look at volatility as something that is temporary, and it usually is.

So while I can’t tell you details about your situation without looking at your data, I can tell you some things it could be.

5 Reasons for Ranking Fluctuations

Here are five primary considerations when you see your traffic or rankings fluctuate or take a dive.

1. Is the Volatility Real?

Eight times out of 10 that a client comes to me in a panic, the problem is with the data itself.

It could be that the tool they use to check rankings is having issues, or that the analytics code somehow got stripped off the pages.

While this may seems obvious, but similar to restarting your computer, check the most obvious things first.

2. What Changes Have Been Made?

Did your server have downtime, or were there other technical issues?

Have you added or deleted a lot of content recently?

3. Look at Your Links

Have you lost any big links recently?

Sometimes just the loss of a highly valuable link can send your rankings into a tailspin.

4. What Has Your Agency Been Doing?

What has your agency been doing on your behalf?

Ask them for details.

Have they been posting a lot of guest posts or buying links on your behalf?

Have they done something else that is outside of Google’s guidelines?

Do you have any manual actions as a result? (Remember, no manual action doesn’t mean there isn’t an algorithmic one)

5. Have You Been Hacked?

It isn’t uncommon for me to find that a site has been compromised in some way when their rankings drop.

Is Your Site Still Relevant?

If you go through all of the issues above and still feel confident that none of them are to blame, then you may need to simply accept that your website is not as relevant or valuable as it once was.

Google’s algorithms are always designed to elevate the best of the best. That doesn’t always happen in practice, but it’s always the goal.

Therefore if you find your own site losing ground, you may need to rethink your strategy and consider that maybe there’s more you need to do to be considered the best.

Based on your original question, which indicated that you’ve been seeing a gradual but consistent decline, the problem most likely is a relevance issue.

Your strategy may be outdated or not keeping pace with the competition.

Hold your agency to task for this. They should be providing you with a detailed plan for how they’re going to turn this downward trend around.

If they aren’t, they may not be the right agency, or you may need to upgrade to a higher level of service with them.

Also, consider that it is not unusual at all for a consultant or other agency to audit a site and strategy even while they are with another agency.

A good audit by an outsider provides a new perspective and can be done in partnership with your agency in most cases.

(I plan to do another post on how to choose an auditor, because I think there’s a lot of “audits” out there that are not helpful.)

Have a question about SEO for Jenny? Fill out this form or use #AskAnSEO on social media.

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Featured Image: Paulo Bobita

Your Computer Is Low On Memory In Windows 11/10

RAM stands for Random Access Memory. It is a volatile memory that stores the data calculated by the CPU. This data is required by programs to show the results as per the commands entered by the users. RAM is an essential hardware component in computers. All the programs use some amount of RAM to function properly. If your computer runs low on memory, the opened programs will not work properly or crash unexpectedly. In this article, we will discuss what you can do if you see the “Your computer is low on memory” or “Out of memory” message on your system.

Your computer is low on memory in Windows 11/10

The error message “Your computer is low on memory” or ” Out of memory” is self-explanatory. Your computer does not have enough memory to run the programs. To resolve this issue, you have to free up memory. You can try the following solutions to resolve this issue.

Check which process is using more memory

Manage unnecessary processes and apps

Increase virtual memory

Make sure all your drivers are up to date

Run Windows Memory Diagnostic tool

Run System Maintenance Troubleshooter

Upgrade your RAM

Let’s see all these fixes in detail.

1] Check which process is using more memory

As explained earlier in the article, all programs require some amount of RAM to run on a computer. The amount of RAM consumed by the programs is not the same. This means that some programs may consume more RAM. If this happens, it creates a problem for the user. It is possible that some programs or services are consuming more RAM on your device. Identify them and kill them if they are not necessary. The steps for the same are explained below:

Open the Task Manager.

Some users have found the RunSWUSB service the culprit of the problem. According to them, the RunSWUSB service was consuming high memory. The issue was fixed when they stopped it. This service is related to the Realtek Network Card driver. If you see this service consuming high RAM, you can disable it.

Read: How to clear Memory Cache in Windows

2] Manage unnecessary processes or apps

It is important to manage unnecessary processes or apps to cut down memory consumption. Startup apps are the apps that start automatically on system startup. These apps keep running in the background and use your system resources. It is important to disable those startup apps that you do not need.

Now, go to the Services tab.

Check the Hide all Microsoft services checkbox. You will see it on the bottom left side.

Restart your computer.

The above action will disable the selected third-party services.

3] Increase virtual memory

One effective solution to resolve this issue is to increase the virtual memory. Virtual memory is also called Page File. Windows uses it in addition to the physical memory or RAM when required.

Read: Fix Your system is running low on virtual memory message on Windows.

4] Make sure all your drivers are up to date

One possible cause of this error is corrupted or outdated drives. Make sure that all your drivers are up to date. Windows 11/10 automatically checks for driver updates. If an update for drivers is available, it is shown on the Optional Updates page in Windows 11/10 Settings. Open the Optional Updates page in Windows 11/10 Settings and see if an update for your drivers is available. If yes, update your drivers and see if this helps.

Read: How to Free up, Reduce or Limit RAM usage in Windows 11

5] Run Windows Memory Diagnostic tool

If the error still appears, you should check if your RAM is working fine or not. When RAM malfunctions, a computer starts showing the following symptoms:

The computer’s performance slows down,

Programs crash unexpectedly or refuse to open,

Multitasking becomes a hard nut to crack for your computer, etc.

In your case, your system shows you symptoms similar to those mentioned above. Hence, you should check your memory by running the Windows Memory Diagnostic tool.

6] Run System Maintenance Troubleshooter

The System Maintenance Troubleshooter detects and fixes common maintenance problems on a Windows PC. Run System Maintenance Troubleshooter and see if it helps fix the memory issue.

Read: The biggest Myths about RAM that many people have

7] Upgrade your RAM

If the issue is not resolved, you need to upgrade your RAM. The low memory problem occurs if you run too many programs on a system with less RAM.

Read: How to Enable or Disable Memory Compression in Windows.

What causes low memory on the computer?

The low memory issue occurs on a computer if the computer runs out of RAM. Every program that you run on your computer consumes some amount of RAM. When you open too many heavy programs, your computer may run out of memory. Opening too many tabs in a web browser also consumes too much RAM. To resolve this problem, you have to free up RAM.

How do I free up RAM?

You can free up RAM by killing unnecessary processes in the Task Manager. But before doing this, make sure that the process is not the Windows process. Killing a Windows process may make your system unstable. The startup apps also consume RAM. Therefore, disable all the startup apps to prevent them from running automatically on system startup.

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