1592936640

# Confusion Matrix is Not so Confusing

Confusion Matrix is a matrix that illustrates the performance of a classification model when exposed to unseen data. This matrix helps us to identify how the model is performing on test set. From this matrix, many other scores are calculated such as Accuracy, Recall, Precision, F1-score, etc. It is important one should know where to use which type of score as it depends on the application.

There are two classes: Class 1 and Class 2

Class 1:Positive

Class 2: Negative

Positive: Observation is True (eg. Picture is a dog)

Negative: Observation is False (eg. Picture is not a dog)

T.P.(True Positive): Truth and Prediction both are Positive

T.N.(True Negative): Truth and Prediction both are Negative

F.P.(False Positive): Truth is Negative but Prediction is Positive

F.N.(False Negative): Truth is Positive but Prediction is Negative

## Accuracy:

Accuracy is the ratio of sum of True Positive(T.P.) and True Negative(T.N.) to the sum of the matrix elements.

## Precision:

Precision is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Positive(F.P)

## Recall:

Recall is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Negative(F.N)

_High recall, low precision: _This means that most of the positive examples are correctly recognized (low FN) but there are a lot of false positives.

_Low recall, high precision: _This shows that we miss a lot of positive examples (high FN) but those we predict as positive are indeed positive (low FP)

## F1-score:

Since we have two measures (Precision and Recall) it helps to have a measurement that represents both of them. We calculate an F-measure which uses Harmonic Mean in place of Arithmetic Mean as it punishes the extreme values more

#artificial-intelligence #python #confusion-matrix #machine-learning #data-science

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## Introduction

Matrix is an ambitious new ecosystem for open federated Instant Messaging and VoIP. The basics you need to know to get up and running are:

• Everything in Matrix happens in a room. Rooms are distributed and do not exist on any single server. Rooms can be located using convenience aliases like `#matrix:matrix.org` or `#test:localhost:8448`.
• Matrix user IDs look like `@matthew:matrix.org` (although in the future you will normally refer to yourself and others using a third party identifier (3PID): email address, phone number, etc rather than manipulating Matrix user IDs)

The overall architecture is:

``````client <----> homeserver <=====================> homeserver <----> client
https://somewhere.org/_matrix      https://elsewhere.net/_matrix
``````

`#matrix:matrix.org` is the official support room for Matrix, and can be accessed by any client from https://matrix.org/docs/projects/try-matrix-now.html or via IRC bridge at irc://irc.libera.chat/matrix.

Synapse is currently in rapid development, but as of version 0.5 we believe it is sufficiently stable to be run as an internet-facing service for real usage!

Matrix specifies a set of pragmatic RESTful HTTP JSON APIs as an open standard, which handle:

• Creating and managing fully distributed chat rooms with no single points of control or failure
• Eventually-consistent cryptographically secure synchronisation of room state across a global open network of federated servers and services
• Sending and receiving extensible messages in a room with (optional) end-to-end encryption
• Inviting, joining, leaving, kicking, banning room members
• Managing user accounts (registration, login, logout)
• Using 3rd Party IDs (3PIDs) such as email addresses, phone numbers, Facebook accounts to authenticate, identify and discover users on Matrix.
• Placing 1:1 VoIP and Video calls

These APIs are intended to be implemented on a wide range of servers, services and clients, letting developers build messaging and VoIP functionality on top of the entirely open Matrix ecosystem rather than using closed or proprietary solutions. The hope is for Matrix to act as the building blocks for a new generation of fully open and interoperable messaging and VoIP apps for the internet.

Synapse is a Matrix "homeserver" implementation developed by the matrix.org core team, written in Python 3/Twisted.

In Matrix, every user runs one or more Matrix clients, which connect through to a Matrix homeserver. The homeserver stores all their personal chat history and user account information - much as a mail client connects through to an IMAP/SMTP server. Just like email, you can either run your own Matrix homeserver and control and own your own communications and history or use one hosted by someone else (e.g. matrix.org) - there is no single point of control or mandatory service provider in Matrix, unlike WhatsApp, Facebook, Hangouts, etc.

We'd like to invite you to join #matrix:matrix.org (via https://matrix.org/docs/projects/try-matrix-now.html), run a homeserver, take a look at the Matrix spec, and experiment with the APIs and Client SDKs.

Thanks for using Matrix!

## Support

For support installing or managing Synapse, please join `#synapse:matrix.org` (from a matrix.org account if necessary) and ask questions there. We do not use GitHub issues for support requests, only for bug reports and feature requests.

Synapse's documentation is nicely rendered on GitHub Pages, with its source available in `docs`.

## Connecting to Synapse from a client

The easiest way to try out your new Synapse installation is by connecting to it from a web client.

Unless you are running a test instance of Synapse on your local machine, in general, you will need to enable TLS support before you can successfully connect from a client: see TLS certificates.

An easy way to get started is to login or register via Element at https://app.element.io/#/login or https://app.element.io/#/register respectively. You will need to change the server you are logging into from `matrix.org` and instead specify a Homeserver URL of `https://<server_name>:8448` (or just `https://<server_name>` if you are using a reverse proxy). If you prefer to use another client, refer to our client breakdown.

If all goes well you should at least be able to log in, create a room, and start sending messages.

### Registering a new user from a client

By default, registration of new users via Matrix clients is disabled. To enable it, specify `enable_registration: true` in `homeserver.yaml`. (It is then recommended to also set up CAPTCHA - see docs/CAPTCHA_SETUP.md.)

Once `enable_registration` is set to `true`, it is possible to register a user via a Matrix client.

Your new user name will be formed partly from the `server_name`, and partly from a localpart you specify when you create the account. Your name will take the form of:

``````@localpart:my.domain.name
``````

(pronounced "at localpart on my dot domain dot name").

As when logging in, you will need to specify a "Custom server". Specify your desired `localpart` in the 'User name' box.

## Security note

Matrix serves raw, user-supplied data in some APIs -- specifically the content repository endpoints.

Whilst we make a reasonable effort to mitigate against XSS attacks (for instance, by using CSP), a Matrix homeserver should not be hosted on a domain hosting other web applications. This especially applies to sharing the domain with Matrix web clients and other sensitive applications like webmail. See https://developer.github.com/changes/2014-04-25-user-content-security for more information.

Ideally, the homeserver should not simply be on a different subdomain, but on a completely different registered domain (also known as top-level site or eTLD+1). This is because some attacks are still possible as long as the two applications share the same registered domain.

To illustrate this with an example, if your Element Web or other sensitive web application is hosted on `A.example1.com`, you should ideally host Synapse on `example2.com`. Some amount of protection is offered by hosting on `B.example1.com` instead, so this is also acceptable in some scenarios. However, you should not host your Synapse on `A.example1.com`.

Note that all of the above refers exclusively to the domain used in Synapse's `public_baseurl` setting. In particular, it has no bearing on the domain mentioned in MXIDs hosted on that server.

Following this advice ensures that even if an XSS is found in Synapse, the impact to other applications will be minimal.

The instructions for upgrading synapse are in the upgrade notes. Please check these instructions as upgrading may require extra steps for some versions of synapse.

## Using a reverse proxy with Synapse

It is recommended to put a reverse proxy such as nginx, Apache, Caddy, HAProxy or relayd in front of Synapse. One advantage of doing so is that it means that you can expose the default https port (443) to Matrix clients without needing to run Synapse with root privileges.

For information on configuring one, see docs/reverse_proxy.md.

## Identity Servers

Identity servers have the job of mapping email addresses and other 3rd Party IDs (3PIDs) to Matrix user IDs, as well as verifying the ownership of 3PIDs before creating that mapping.

They are not where accounts or credentials are stored - these live on home servers. Identity Servers are just for mapping 3rd party IDs to matrix IDs.

This process is very security-sensitive, as there is obvious risk of spam if it is too easy to sign up for Matrix accounts or harvest 3PID data. In the longer term, we hope to create a decentralised system to manage it (matrix-doc #712), but in the meantime, the role of managing trusted identity in the Matrix ecosystem is farmed out to a cluster of known trusted ecosystem partners, who run 'Matrix Identity Servers' such as Sydent, whose role is purely to authenticate and track 3PID logins and publish end-user public keys.

You can host your own copy of Sydent, but this will prevent you reaching other users in the Matrix ecosystem via their email address, and prevent them finding you. We therefore recommend that you use one of the centralised identity servers at `https://matrix.org` or `https://vector.im` for now.

To reiterate: the Identity server will only be used if you choose to associate an email address with your account, or send an invite to another user via their email address.

Users can reset their password through their client. Alternatively, a server admin can reset a users password using the admin API or by directly editing the database as shown below.

First calculate the hash of the new password:

``````\$ ~/synapse/env/bin/hash_password
\$2a\$12\$xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
``````

Then update the `users` table in the database:

``````UPDATE users SET password_hash='\$2a\$12\$xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
WHERE name='@test:test.com';
``````

## Synapse Development

The best place to get started is our guide for contributors. This is part of our larger documentation, which includes information for synapse developers as well as synapse administrators.

Developers might be particularly interested in:

Alongside all that, join our developer community on Matrix: #synapse-dev:matrix.org, featuring real humans!

### Quick start

Before setting up a development environment for synapse, make sure you have the system dependencies (such as the python header files) installed - see Platform-specific prerequisites.

To check out a synapse for development, clone the git repo into a working directory of your choice:

``````git clone https://github.com/matrix-org/synapse.git
cd synapse
``````

Synapse has a number of external dependencies. We maintain a fixed development environment using Poetry. First, install poetry. We recommend:

``````pip install --user pipx
pipx install poetry
``````

as described here. (See poetry's installation docs for other installation methods.) Then ask poetry to create a virtual environment from the project and install Synapse's dependencies:

``````poetry install --extras "all test"
``````

This will run a process of downloading and installing all the needed dependencies into a virtual env.

We recommend using the demo which starts 3 federated instances running on ports 8080 - 8082:

``````poetry run ./demo/start.sh
``````

(to stop, you can use `poetry run ./demo/stop.sh`)

If you just want to start a single instance of the app and run it directly:

``````# Create the homeserver.yaml config once
poetry run synapse_homeserver \
--server-name my.domain.name \
--config-path homeserver.yaml \
--generate-config \
--report-stats=[yes|no]

# Start the app
poetry run synapse_homeserver --config-path homeserver.yaml
``````

### Running the unit tests

After getting up and running, you may wish to run Synapse's unit tests to check that everything is installed correctly:

``````poetry run trial tests
``````

This should end with a 'PASSED' result (note that exact numbers will differ):

``````Ran 1337 tests in 716.064s

PASSED (skips=15, successes=1322)
``````

For more tips on running the unit tests, like running a specific test or to see the logging output, see the CONTRIBUTING doc.

### Running the Integration Tests

Synapse is accompanied by SyTest, a Matrix homeserver integration testing suite, which uses HTTP requests to access the API as a Matrix client would. It is able to run Synapse directly from the source tree, so installation of the server is not required.

Testing with SyTest is recommended for verifying that changes related to the Client-Server API are functioning correctly. See the SyTest installation instructions for details.

## Platform dependencies

Synapse uses a number of platform dependencies such as Python and PostgreSQL, and aims to follow supported upstream versions. See the docs/deprecation_policy.md document for more details.

## Troubleshooting

### Running out of File Handles

If synapse runs out of file handles, it typically fails badly - live-locking at 100% CPU, and/or failing to accept new TCP connections (blocking the connecting client). Matrix currently can legitimately use a lot of file handles, thanks to busy rooms like #matrix:matrix.org containing hundreds of participating servers. The first time a server talks in a room it will try to connect simultaneously to all participating servers, which could exhaust the available file descriptors between DNS queries & HTTPS sockets, especially if DNS is slow to respond. (We need to improve the routing algorithm used to be better than full mesh, but as of March 2019 this hasn't happened yet).

If you hit this failure mode, we recommend increasing the maximum number of open file handles to be at least 4096 (assuming a default of 1024 or 256). This is typically done by editing `/etc/security/limits.conf`

Separately, Synapse may leak file handles if inbound HTTP requests get stuck during processing - e.g. blocked behind a lock or talking to a remote server etc. This is best diagnosed by matching up the 'Received request' and 'Processed request' log lines and looking for any 'Processed request' lines which take more than a few seconds to execute. Please let us know at #synapse:matrix.org if you see this failure mode so we can help debug it, however.

### Help!! Synapse is slow and eats all my RAM/CPU!

First, ensure you are running the latest version of Synapse, using Python 3 with a PostgreSQL database.

Synapse's architecture is quite RAM hungry currently - we deliberately cache a lot of recent room data and metadata in RAM in order to speed up common requests. We'll improve this in the future, but for now the easiest way to either reduce the RAM usage (at the risk of slowing things down) is to set the almost-undocumented `SYNAPSE_CACHE_FACTOR` environment variable. The default is 0.5, which can be decreased to reduce RAM usage in memory constrained enviroments, or increased if performance starts to degrade.

However, degraded performance due to a low cache factor, common on machines with slow disks, often leads to explosions in memory use due backlogged requests. In this case, reducing the cache factor will make things worse. Instead, try increasing it drastically. 2.0 is a good starting value.

Using libjemalloc can also yield a significant improvement in overall memory use, and especially in terms of giving back RAM to the OS. To use it, the library must simply be put in the LD_PRELOAD environment variable when launching Synapse. On Debian, this can be done by installing the `libjemalloc1` package and adding this line to `/etc/default/matrix-synapse`:

``````LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.1
``````

This can make a significant difference on Python 2.7 - it's unclear how much of an improvement it provides on Python 3.x.

If you're encountering high CPU use by the Synapse process itself, you may be affected by a bug with presence tracking that leads to a massive excess of outgoing federation requests (see discussion). If metrics indicate that your server is also issuing far more outgoing federation requests than can be accounted for by your users' activity, this is a likely cause. The misbehavior can be worked around by setting the following in the Synapse config file:

``````presence:
enabled: false``````

### People can't accept room invitations from me

The typical failure mode here is that you send an invitation to someone to join a room or direct chat, but when they go to accept it, they get an error (typically along the lines of "Invalid signature"). They might see something like the following in their logs:

``````2019-09-11 19:32:04,271 - synapse.federation.transport.server - 288 - WARNING - GET-11752 - authenticate_request failed: 401: Invalid signature for server <server> with key ed25519:a_EqML: Unable to verify signature for <server>
``````

This is normally caused by a misconfiguration in your reverse-proxy. See docs/reverse_proxy.md and double-check that your settings are correct.

Author: matrix-org
Source Code: https://github.com/matrix-org/synapse

#python

1592936640

## Confusion Matrix is Not so Confusing

Confusion Matrix is a matrix that illustrates the performance of a classification model when exposed to unseen data. This matrix helps us to identify how the model is performing on test set. From this matrix, many other scores are calculated such as Accuracy, Recall, Precision, F1-score, etc. It is important one should know where to use which type of score as it depends on the application.

There are two classes: Class 1 and Class 2

Class 1:Positive

Class 2: Negative

Positive: Observation is True (eg. Picture is a dog)

Negative: Observation is False (eg. Picture is not a dog)

T.P.(True Positive): Truth and Prediction both are Positive

T.N.(True Negative): Truth and Prediction both are Negative

F.P.(False Positive): Truth is Negative but Prediction is Positive

F.N.(False Negative): Truth is Positive but Prediction is Negative

## Accuracy:

Accuracy is the ratio of sum of True Positive(T.P.) and True Negative(T.N.) to the sum of the matrix elements.

## Precision:

Precision is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Positive(F.P)

## Recall:

Recall is defined as the ratio of True Positive(T.P) to the sum of True Positive(T.P) and False Negative(F.N)

_High recall, low precision: _This means that most of the positive examples are correctly recognized (low FN) but there are a lot of false positives.

_Low recall, high precision: _This shows that we miss a lot of positive examples (high FN) but those we predict as positive are indeed positive (low FP)

## F1-score:

Since we have two measures (Precision and Recall) it helps to have a measurement that represents both of them. We calculate an F-measure which uses Harmonic Mean in place of Arithmetic Mean as it punishes the extreme values more

#artificial-intelligence #python #confusion-matrix #machine-learning #data-science

1597940460

## Confusion Matrix is no more confusing.

Before we switch into the topic, lets understand why we need to consider Confusion matrix and metrics ?

Metrics plays a major role in evaluating the performance of the model.

Metrics from Confusion Matrix.

• Confusion Matrix (Precision, Recall, F score, Accuracy)

Confusion Matrix is no more Confusing.

Consider a dataset has two classes say Class A and B. There may be two cases where your dataset is **Balanced **and Imbalanced. Balanced dataset means that, records for class A and B are balanced. Say Class A has 50% of data and class B has 50% of data or 55–45% of data. Imbalanced dataset has records of 90–10% of Class A and B or 80–20 and 70–30% of data.

Metrics to consider will be different for both Balanced and Imbalanced dataset.

Confusion Matrix comes with rows and columns of Actual and Predicted. The terminologies used are True Positive, True Negative, False positive, False Negative.

Lets split the words as True and positive separately.

Positive : Class A ; Negative : Not a Class A(Class B)

True : Predicted is right ; False : Predicted is wrong

True Positive : _Positive _: Model predicted as Class A, **_True _**what model predicted is correct. Concludes as : Actual is Class A, Model Predicted as Class A.

True Negative : _Negative _: Model predicted as Class B, **_True _**what model predicted is correct.

#sklearn-metrics #confusion-matrix #metrics #precision-recall #imbalanced-data #data analytic

1582888330

## Learning to Design a School Performance Matrix | Simpliv

Description
The module “Designing School Performance Matrix” figures towards identification of important aspects towards Quality Initiatives in the schools on ground of Quality Requisites as a priority. The course modulates the connect towards important dimensions to the schools with contributions and recognition required to each in particular. The aspects relate parenting, classroom management, beneficiary satisfaction and the contribution to teaching learning practices in totality.

Who is the target audience?

Basic knowledge
Basic Requirement :

ICT Expertise
What will you learn
A Ready Reckoner Towards Effective School Management

ENROLL

#Designing School Performance Matrix #Learning to Design a School Performance Matrix | Simpliv #Matrix Courses #School Design Matrix

1600289880

## Let's make the “Confusion matrix” less confusing!!

“Doubts are good. Confusion is excellent. Questions are awesome.

All these are attempts to expand the wisdom of mind.”

_― _Manoj Arora

In a classification problem, it is often important to specify the performance assessment. This can be valuable when the cost of different misclassifications varies significantly. Classification accuracy is also a measure showing how well the classifier correctly identifies the objects.

A confusion matrix also called a contingency table or error matrix gets across the picture when it comes to visualizing the performance of a classifier. The columns of the matrix represent the instances of the predicted classes and the rows represent the instances of the actual class. (Note: It can be the other way around as well.)

The confusion matrix shows the ways in which your classification model is confused when it makes predictions.

#machine-learning #data-science #confusion-matrix #hypothesis-testing #python