ΙΣΤΟΤΟΠΟΣ 2ης ΠΕΡΙΦΕΡΕΙΑΣ ΣΧΟΛΙΚΟΥ ΣΥΜΒΟΥΛΟΥ ΔΗΜΟΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ ΗΛΕΙΑΣ

п»їMicrosoft Secure Score.
Following the Secure Score recommendations can protect your organization from threats. From a centralized dashboard in the Microsoft 365 security center, organizations can monitor and work on the security of their Microsoft 365 identities, apps, and devices.
Secure Score helps organizations:
Report on the current state of the organization's security posture. Improve their security posture by providing discoverability, visibility, guidance, and control. Compare with benchmarks and establish key performance indicators (KPIs).
Organizations gain access to robust visualizations of metrics and trends, integration with other Microsoft products, score comparison with similar organizations, and much more. The score can also reflect when third-party solutions have addressed recommended actions.
How it works.
You're given points for the following actions:
Configuring recommended security features Doing security-related tasks Addressing the improvement action with a third-party application or software, or an alternate mitigation.
Some improvement actions only give points when fully completed. Some give partial points if they're completed for some devices or users. If you can't or don't want to enact one of the improvement actions, you can choose to accept the risk or remaining risk.
If you have a license for one of the supported Microsoft products, then you'll see recommendations for those products. We show you the full set of possible improvements for a product, regardless of license edition, subscription, or plan. This way, you can understand security best practices and improve your score. Your absolute security posture, represented by Secure Score, stays the same no matter what licenses your organization owns for a specific product. Keep in mind that security should be balanced with usability, and not every recommendation can work for your environment.
Your score is updated in real time to reflect the information presented in the visualizations and improvement action pages. Secure Score also syncs daily to receive system data about your achieved points for each action.
Key scenarios.
Check your current score Compare your score to organizations like yours View improvement actions and decide an action plan Initiate work flows to investigate or implement.
How improvement actions are scored.
Each improvement action is worth 10 points or less, and most are scored in a binary fashion. If you implement the improvement action, like create a new policy or turn on a specific setting, you get 100% of the points. For other improvement actions, points are given as a percentage of the total configuration.
For example, an improvement action states you get 10 points by protecting all your users with multi-factor authentication. You only have 50 of 100 total users protected, so you'd get a partial score of 5 points (50 protected / 100 total * 10 max pts = 5 pts).
Products included in Secure Score.
Currently there are recommendations for the following products:
Microsoft 365 (including Exchange Online) Azure Active Directory Microsoft Defender for Endpoint Microsoft Defender for Identity Cloud App Security Microsoft Teams.
Recommendations for other security products are coming soon. The recommendations won't cover all the attack surfaces associated with each product, but they're a good baseline. You can also mark the improvement actions as covered by a third party or alternate mitigation.
Security defaults.
Microsoft Secure Score has updated improvement actions to support security defaults in Azure Active Directory, which make it easier to help protect your organization with pre-configured security settings for common attacks.
If you turn on security defaults, you'll be awarded full points for the following improvement actions:
Ensure all users can complete multi-factor authentication for secure access (9 points) Require MFA for administrative roles (10 points) Enable policy to block legacy authentication (7 points)
Security defaults include security features that provide similar security to the "sign-in risk policy" and "user risk policy" improvement actions. Instead of setting up these policies on top of the security defaults, we recommend updating their statuses to "Resolved through alternative mitigation."
Required permissions.
To have permission to access Microsoft Secure Score, you must be assigned one of the following roles in Azure Active Directory.
Read and write roles.
With read and write access, you can make changes and directly interact with Secure Score. You can also assign read-only access to other users.
Global administrator Security administrator Exchange administrator SharePoint administrator Account administrator.
Read-only roles.
With read-only access, you aren't able to edit status or notes for an improvement action, edit score zones, or edit custom comparisons.
Helpdesk administrator User administrator Service administrator Security reader Security operator Global reader.
Risk awareness.
Microsoft Secure Score is a numerical summary of your security posture based on system configurations, user behavior, and other security-related measurements. It isn't an absolute measurement of how likely your system or data will be breached. Rather, it represents the extent to which you have adopted security controls in your Microsoft environment that can help offset the risk of being breached. No online service is immune from security breaches, and secure score shouldn't be interpreted as a guarantee against security breach in any manner.
We want to hear from you.
If you have any issues, let us know by posting in the Security, Privacy & Compliance community. We're monitoring the community and will provide help.


Correct Score Prediction & Picks.
Get all the information you need for placing bets on correct score Market.
Correct Score Tips for Today.
Tottenham vs Chelsea.
Tottenham 0-2 Chelsea at 8/1 with 10 Bet.
SC Farense vs Santa Clara.
SC Farense 2-1 Santa Clara at 9/1 with Betway.
Belenenses vs FC Porto.
Belenenses 0-3 FC Porto at 15/2 with Bet365.
Braga vs Portimonense.
Braga 3-0 Portimonense at 8/1 with 888sports.
FC Famalicao vs Moreirense.
FC Famalicao 2-1 Moreirense at 15/2 with Ladbrokes.
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Correct Score Betting Tips.
Correct Score bets are some of the most popular football bets to place, and yet they are also some of the hardest to get right. As a result, the odds – and potential rewards – when you do can mean a decent pay-out in your favour.
What is a Correct Score Bet?
A Correct Score bet, unsurprisingly, is a single bet on the final score of a game. As mentioned, it is one of the hardest bets to get right, but as a result you will get good odds on each selection.
Usually, the odds are shorter for lower scoring games – 0-0, 1-0, 0-1, 1-1 – and they increase gradually as you bet on more goals being scored.
Why bet on Correct Score?
While they are the hardest to get right, and you can often be waiting for some time for a payout, thanks to the increased odds available for a correct score bet the payouts are often well worth waiting for.
Providing you bet sensibly, you only need a handful of correct scores to payout each season to finish the year in profit and, as we will cover below, there are plenty of ways to improve your odds of getting a correct score bet right.
Correct Score Tips: Bet types and how to improve your potential returns.
It is difficult to correctly predict the exact score of a football match, but there are many ways you can improve your chances of placing a successful bet. Study the form, look for games where less goals are likely to be scored and look at leagues where match outcomes tend to be easier to predict. Let’s take a closer look…
What to look out for when betting on Correct Score.
One of the first things you do when betting on the correct score of a football match is avoid picking games where you think plenty of goals will be scored.
Of course, the bigger scorelines will return greater odds and more potential profit, but there’s a reason for that – it’s less likely to be successful. Even if you think a match will be a one-sided affair, with one team dominating, there are so many different variations of how that scoreline – even if you predicted the outcome (i.e. a big win for one team) correctly – could look. Anything, really, above a 3-0 win would be considered a big victory. Will the team step off the gas after building such a lead? Will the opposing team snatch a consolation? The number of scorelines that would be considered ‘high-scoring’ are vast, whereas if you correctly pick a low-scoring game, there are only really four outcomes to look out for.
Low-scoring matches.
Study the form book, and if you have found two teams playing each other who tend not to score – or concede – many goals, you have a good starting point for a successful correct score bet.
For starters, a game with few goals will finish one of four ways – 0-0, 1-0, 0-1, 1-1. If you are correct in predicting the low-scoring game, there is a one-in-four chance, effectively, that you will then have picked the correct scoreline too.
The odds are lower for those scorelines, but given 1,782 matches finished 1-0 to either side in the first 25 years of the Premier League – no other outcome occurred more than 1500 times – you can see there are going to be more payouts. For the record, in that period, 1,140 finished 1-1 and 828 were goalless draws.
However, we would advise normally looking away from the Premier League (see below) due to the more unpredictable nature of the league compared to some of EuropeРІР‚в„ўs other top divisions.
Correctly identifying a low-scoring match also offers the chance of a consolation should you bet through Bet365 too – we’ve detailed how to take advantage of their "Bore Draw Money Back" offer below.
But where should you be looking for these low-scoring matches?
Regimented leagues.
The Premier League is often viewed as being the best league in the world, by commentators prone to hyperbole, thanks to its unpredictable nature. High-scoring games are common and, as many managers like to point out ‘anyone can beat anyone’. Of course, that bit is true for every league, but in the Premier League it seems to ring true more often.
Instead, you need to find leagues where low-scoring games are common – the German Bundesliga, for example. With teams known typically for their organised defences and regimented formations, just less than 25 per cent of all matches played before the winter break in the 2017/18 finished either 1-0 to the home team or 1-1.
Both teams to score; over 2.5 goals in match.
Another popular type of football bet is a combined BTTS/over 2.5 goals in match bet, and when predicting the correct score a 2-1 win is another result worth betting on.
Indeed, in the first 25 years of the Premier League as we quoted above, the second most common scoreline was 2-1 – occurring 1,455 times.
If you choose the BTTS/more than 2.5 goals in match bet, you will likely get slightly lower odds than if you specifically back a team to win 2-1 – and yet it is a relatively common scoreline.
In La Liga, 14 per cent of matches in the 2017/18 season – at the time of writing – finished 2-1 to either the home or away team, while in the Scottish Premiership that total goes up to more than 18 per cent; on average, that means one team every weekend in the Scottish Premiership will win a game 2-1. And yet you can still get odds of around 8/1-9/1 of the favourites winning 2-1 in a typical Scottish Premiership match market.
Picking a 2-1 scoreline can also keep your bet alive for longer than if you back a 1-0. No matter who scores first, your bet still has a chance of winning. ItРІР‚в„ўs no use if it ultimately fails, but nobody wants to be reaching half-time or even earlier on match day having already had to tear up their betting slip.
Pick a score, and stick with it.
Once you have identified the potential low-scoring games, it is best to stick with a scoreline when placing correct score bets.
Sure, the chance of every game you bet on finishing with that scoreline is extremely low, but some are likely too at least meaning you will get at least some payouts if you pick your games well. Constantly change your predicted score around and, in actual fact, you are reducing the probability of your correct score bet coming off.
How can you take advantage of Bet365РІР‚в„ўs Bore Draw Money Back Offer?
We have already mentioned the bonus of a consolation if you pick a low-scoring game through Bet365.
Bet365 offer money back on any Correct Score, Half-Time/Full-Time or Scorecast bets if the match in question finishes 0-0. So if you have correctly identified a low-scoring game, that ends up finishing goalless, you will get your money back at least – even if your 1-0 Correct Score bet is therefore not successful.


Test Grade Calculator.
If you're looking for a tool which can help you in setting a grading scale, this test grade calculator is a must. Also known as test score calculator or teacher grader , this tool quickly finds out the grade and percentage on the basis of the number of points and wrong (or correct) answers. Moreover, you can change the default grading scale and set your own one. Ar you still wondering how to calculate test score? Scroll down to find out - or simply experiment with this grading scale calculator.
If this test grade calculator is not the tool you're exactly looking for, check out our other grading calculators like the high school GPA calculator with many weighting options, as well as the complementary college GPA calculator. Also, you may find the final grade tool useful to check what your final grade will be - or what you can do to improve it.
Besides, if you are considering to take a student loan, check out our student loan calculator where you can make a projection on your expenses and study the effect of different student loan options on your budget.
How to calculate test score.
To calculate the percentile test score, all you need to do is divide the earned points by the total points possible. In other words, you're simply finding the percentage of good answers:
percentage score = #correct / #total.
percentage score = (#total - #wrong) / #total.
Then, all you need to do is convert the percentage score into a letter grade. The default grading scale looks as in the table below:
If you don't using the +/- grades, the scale may look like:
An A is 90% to 100% A B is 80% to 89% A C is 70% to 79% A D is 60% to 69% and finally an F is 59% and below - and it's not a passing grade.
Above you could find the standard grading system for US schools and universities. However, the grading may vary among schools, classes and teachers. Always check beforehand which system is used in your case.
Sometimes the border of passing score is not 60%, but e.g. 50 or 65%. What then? We've got you covered - you can change the ranges of grades! Read more about it in the last paragraph of this article: Advanced mode options.
Test grade calculator - how to use it?
Our test score calculator is a straightforward and intuitive tool!
Enter the number of questions/points/problems in the student's work (test, quiz, exam - anything). Assume you've prepared the test with 18 questions.
Type in the number the student got wrong . Instead - if you prefer - you can enter the number of gained points. Let's say our exemplary student failed to answer three questions.
Here we go! Teacher grader tool is showing the percentage and grade for that score. For our example, the student got a score of 83.33% from a test, which corresponds to B grade.
Underneath you'll find a full grading scale table . So to check the score for the next students, you can type in the number of questions they've got wrong - or just use this neat table.
Test grade calculator - advanced mode options.
That was a basic version of the calculations. But our teacher grader is a much more versatile and flexible tool! You can choose more options to customize this test score calculator. Just hit the Advanced mode button below the tool, and two more options will appear:
Increment by box - here you can change the look of the table which you get as a result. The default value is 1, which means that the student can get an integer number of points. But sometimes it's possible to get, e.g. half-points - then you can use this box to declare the increment between next scores.
Percentage scale - in that set of boxes, you can change the grading scale from the default one. For example, assume that the test was really difficult and you'd like to change the scale so that getting 50% is already a passing grade (usually it's 60% or even 65%). Change the last box Grade D- ≥ value from default 60% to 50% to reach the goal. You can also change the other ranges if you want to.
And what if I don't need +/- grades ? Well, then just ignore the signs


O que e correct score.
Today we’re going to introduce some terms that are important to machine learning:
Variance r2 score Mean square error.
We illustrate these concepts using scikit-learn.
(This article is part of our scikit-learn Guide. Use the right-hand menu to navigate.)
Why these terms are important.
You need to understand these metrics in order to determine whether regression models are accurate or misleading. Following a flawed model is a bad idea, so it is important that you can quantify how accurate your model is. Understanding that is not so simple.
These first metrics are just a few of them. Other concepts, like bias and overtraining models, also yield misleading results and incorrect predictions.
To provide examples, let’s use the code from our last blog post, and add additional logic. We’ll also introduce some randomness in the dependent variable ( y ) so that there is some error in our predictions. (Recall that, in the last blog post we made the independent y and dependent variables x perfectly correlate to illustrate the basics of how to do linear regression with scikit-learn.)
What is variance?
In terms of linear regression, variance is a measure of how far observed values differ from the average of predicted values, i.e., their difference from the predicted value mean . The goal is to have a value that is low. What low means is quantified by the r2 score (explained below).
In the code below, this is np.var(err) , where err is an array of the differences between observed and predicted values and np.var() is the numpy array variance function.
What is r2 score?
The r2 score varies between 0 and 100%. It is closely related to the MSE (see below), but not the same. Wikipedia defines r2 as.
” …the proportion of the variance in the dependent variable that is predictable from the independent variable(s).”
Another definition is “(total variance explained by model) / total variance.” So if it is 100%, the two variables are perfectly correlated, i.e., with no variance at all. A low value would show a low level of correlation, meaning a regression model that is not valid, but not in all cases.
Reading the code below, we do this calculation in three steps to make it easier to understand. g is the sum of the differences between the observed values and the predicted ones. (ytest – preds) **2 . y is each observed value y minus the average of observed values np.mean(ytest) . And then the results are printed thus:
Our goal here is to explain. We can of course let scikit-learn to this with the r2_score() method:
What is mean square error (MSE)?
Mean square error (MSE) is the average of the square of the errors. The larger the number the larger the error. Error in this case means the difference between the observed values y1, y2, y3, … and the predicted ones pred(y1), pred(y2), pred(y3), … We square each difference (pred(yn) – yn)) ** 2 so that negative and positive values do not cancel each other out.
The complete code.
So here is the complete code:
There is no correct value for MSE . Simply put, the lower the value the better and 0 means the model is perfect. Since there is no correct answer, the MSE’s basic value is in selecting one prediction model over another.
Similarly, there is also no correct answer as to what R2 should be. 100% means perfect correlation. Yet, there are models with a low R2 that are still good models.
Our take away message here is that you cannot look at these metrics in isolation in sizing up your model. You have to look at other metrics as well, plus understand the underlying math. We will get into all of this in subsequent blog posts.
Additional Resources.
Learn ML with our free downloadable guide.
This e-book teaches machine learning in the simplest way possible. This book is for managers, programmers, directors – and anyone else who wants to learn machine learning. We start with very basic stats and algebra and build upon that.
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
BMC Bring the A-Game.
From core to cloud to edge, BMC delivers the software and services that enable nearly 10,000 global customers, including 84% of the Forbes Global 100, to thrive in their ongoing evolution to an Autonomous Digital Enterprise. Learn more about BMC ›


O que e correct score.
Today we’re going to introduce some terms that are important to machine learning:
Variance r2 score Mean square error.
We illustrate these concepts using scikit-learn.
(This article is part of our scikit-learn Guide. Use the right-hand menu to navigate.)
Why these terms are important.
You need to understand these metrics in order to determine whether regression models are accurate or misleading. Following a flawed model is a bad idea, so it is important that you can quantify how accurate your model is. Understanding that is not so simple.
These first metrics are just a few of them. Other concepts, like bias and overtraining models, also yield misleading results and incorrect predictions.
To provide examples, let’s use the code from our last blog post, and add additional logic. We’ll also introduce some randomness in the dependent variable ( y ) so that there is some error in our predictions. (Recall that, in the last blog post we made the independent y and dependent variables x perfectly correlate to illustrate the basics of how to do linear regression with scikit-learn.)
What is variance?
In terms of linear regression, variance is a measure of how far observed values differ from the average of predicted values, i.e., their difference from the predicted value mean . The goal is to have a value that is low. What low means is quantified by the r2 score (explained below).
In the code below, this is np.var(err) , where err is an array of the differences between observed and predicted values and np.var() is the numpy array variance function.
What is r2 score?
The r2 score varies between 0 and 100%. It is closely related to the MSE (see below), but not the same. Wikipedia defines r2 as.
” …the proportion of the variance in the dependent variable that is predictable from the independent variable(s).”
Another definition is “(total variance explained by model) / total variance.” So if it is 100%, the two variables are perfectly correlated, i.e., with no variance at all. A low value would show a low level of correlation, meaning a regression model that is not valid, but not in all cases.
Reading the code below, we do this calculation in three steps to make it easier to understand. g is the sum of the differences between the observed values and the predicted ones. (ytest – preds) **2 . y is each observed value y minus the average of observed values np.mean(ytest) . And then the results are printed thus:
Our goal here is to explain. We can of course let scikit-learn to this with the r2_score() method:
What is mean square error (MSE)?
Mean square error (MSE) is the average of the square of the errors. The larger the number the larger the error. Error in this case means the difference between the observed values y1, y2, y3, … and the predicted ones pred(y1), pred(y2), pred(y3), … We square each difference (pred(yn) – yn)) ** 2 so that negative and positive values do not cancel each other out.
The complete code.
So here is the complete code:
There is no correct value for MSE . Simply put, the lower the value the better and 0 means the model is perfect. Since there is no correct answer, the MSE’s basic value is in selecting one prediction model over another.
Similarly, there is also no correct answer as to what R2 should be. 100% means perfect correlation. Yet, there are models with a low R2 that are still good models.
Our take away message here is that you cannot look at these metrics in isolation in sizing up your model. You have to look at other metrics as well, plus understand the underlying math. We will get into all of this in subsequent blog posts.
Additional Resources.
Learn ML with our free downloadable guide.
This e-book teaches machine learning in the simplest way possible. This book is for managers, programmers, directors – and anyone else who wants to learn machine learning. We start with very basic stats and algebra and build upon that.
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
BMC Bring the A-Game.
From core to cloud to edge, BMC delivers the software and services that enable nearly 10,000 global customers, including 84% of the Forbes Global 100, to thrive in their ongoing evolution to an Autonomous Digital Enterprise. Learn more about BMC ›




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