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Microsoft is one of the biggest brands in the tech industry. 

Since our childhood, we have used Microsoft Windows and other applications of this organization and we have also seen the growth of Microsoft's DOS to Windows 11.

But recently Microsoft seems to be losing interest in making its own devices better. Maybe it is because of the reason that its leadership is more interested in AI-based research and other technological innovations, that they are not able to focus on one of the fundamental products which are its "Operating System".

Having said that there are several reasons why Microsoft Windows seems to be outdated these days;

1. Really bad application installers:
   For ages, we have been using Microsoft Windows-based laptops and desktops, still installing an application always seems as if we need to be a computer expert to do so. The wizard-based application installer is not of much help either. For a common Windows user, either an application is easy to install or it is not possible at all.
    
2. Bad files management system:
   I don't know what Microsoft was thinking while making those folder trees that seem to be in-depth. The C: (C-drive) is one such place where nobody wants to explore anything because everything looks so messy. Even if someone gathered the courage to check out C-drive to find any issue in any application, it still takes ages to find specific program files.
   
   Now people who love Windows will say that I am a newbie and that's why I am ranting about these things, but I have been using Windows since 1995 and still struggle through those drives.
    
3. Bad Backups:
   Recently Microsoft seems to have taken backups in One Drive, which is obviously limited and would require us to pay more for more storage, and to upload all the backups, we need to utilize our own bandwidth. It could have been as simple as Mac using its Time Travel feature in which we can backup everything in an external drive. Still, we need to have sophisticated software to do the same.
    
4. Bad Memory Management and Storage:
   Everything that we install on our PCs gets saved somewhere in C-drive, even if we have uninstalled that application. Over time it causes issues because our main memory is always filled after a certain time of use, and it also becomes difficult to install new OS updates like Windows 11.
   
   Having said that, an application's files might not only be in program files but also sometimes under our username-specific folders. This creates confusion as to what needs to be removed and what does not. Also, keyboard, printer, and mouse drivers are installed. Over the last few years of use, I have gathered around 13 GB of driver data from the use of a Keyboard, printer, and mouse, which obviously hinders the performance of the computer and it is difficult for a not-that-tech guy to get rid of those memory eating software or drivers.

But why is this happening with Microsoft Windows?
Technology has always moved from complex to simpler versions.

Initially, we had Android and Mac devices that had a few complex interfaces, but as time passed by, these devices upgraded themselves with a more simple design and interface.

The same didn't happen with Windows devices. Most of the software that was built for Windows devices was heavy and resource intensive. Similarly, the computers that we currently use that are running on the Windows operating system, although very useful, still they are more complex to understand and operate rather than using a Mac or Android device.

Now I am not saying that Android or Macs can overnight change the demography and dominate the tech market, but if the updated trajectory of Windows remained the same, I think other operating systems will overtake Microsoft in this race, and we can already see issues that have started to creep into Windows-based applications.

We can see there are issues with VBA that were once being used in several MS Office software, but now there is software that are providing a better interface and coding ability to deploy web applications. Collaboration has always been difficult in Microsoft Office Applications. On the other hand, Google Sheets and other software have made it much easier to collaborate as a team.

Still saying that Microsoft Windows will fade away will not be correct because Microsoft as an organization will be having some plans for the same. But it is obvious that Microsoft seems to be losing interest in updating Windows to be more efficient platform as per my views.

In the context of machine learning models, precision, recall, and F1 score are commonly used evaluation metrics that help assess the performance of a classifier, particularly in binary classification tasks (where there are two classes: positive and negative). These metrics are based on the concept of confusion matrix, which summarizes the performance of a classification model by comparing its predictions against the actual labels.

Here are the definitions of each metric:
1. Precision: Precision, also known as positive predictive value, measures the proportion of true positive predictions among all positive predictions made by the model. It is calculated as:

Precision = True Positives / (True Positives + False Positives)

High precision indicates that when the model predicts a positive class, it is usually correct.

2. Recall: Recall, also known as sensitivity or true positive rate, measures the proportion of true positive predictions among all actual positive instances in the dataset. It is calculated as:

Recall = True Positives / (True Positives + False Negatives)

High recall indicates that the model can correctly identify a large portion of the positive instances in the dataset.

3. F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a balanced evaluation metric that considers both precision and recall. The formula to calculate the F1 score is:

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

The F1 score combines precision and recall, making it more appropriate when you need to strike a balance between avoiding false positives (low precision) and false negatives (low recall).

When to use each metric:

• Precision is more appropriate when the cost of false positives is high. For example, in the context of medical diagnosis, false positives may lead to unnecessary treatments or interventions.

• Recall is more appropriate when the cost of false negatives is high. For instance, in fraud detection, missing a fraudulent transaction (false negative) can lead to financial losses.

• F1 score is useful when both false positives and false negatives have significant consequences and you want a balanced evaluation metric.

It's important to note that the choice of the appropriate metric depends on the specific requirements and objectives of the machine learning task, and in some cases, it may be necessary to consider multiple metrics to gain a comprehensive understanding of the model's performance. Additionally, these metrics are not restricted to binary classification and can be adapted to multiclass problems using various techniques like micro-averaging or macro-averaging.