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One the most fundamental concepts in Probability, Statistics and Bayesian Statistics is Conditional Probability. In this StatQuest, we walk you through what Conditional Probabilities are and how to calculate them using raw data as well as unconditional probabilities. BAM!!!

Subscribe: https://www.youtube.com/c/joshstarmer/featured

#machine-learning

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As artificial intelligence (AI) models, especially those using deep learning, have gained prominence over the last eight or so years [8], they are now significantly impacting society, ranging from loan decisions to self-driving cars. Inherently though, a majority of these models are opaque, and hence following their recommendations blindly in human critical applications can raise issues such as fairness, safety, reliability, along with many others. This has led to the emergence of a subfield in AI called explainable AI (XAI) [7]. XAI is primarily concerned with understanding or interpreting the decisions made by these opaque or black-box models so that one can appropriate trust, and in some cases, have even better performance through human-machine collaboration [5].

While there are multiple views on what XAI is [12] and how explainability can be formalized [4, 6], it is still unclear as to what XAI truly is and why it is hard to formalize mathematically. The reason for this lack of clarity is that not only must the model and/or data be considered but also the final consumer of the explanation. Most XAI methods [11, 9, 3], given this intermingled view, try to meet all these requirements at the same time. For example, many methods try to identify a sparse set of features that replicate the decision of the model. The sparsity is a proxy for the consumer’s mental model. An important question asks whether we can disentangle the steps that XAI methods are trying to accomplish? This may help us better understand the truly challenging parts as well as the simpler parts of XAI, not to mention it may motivate different types of methods.

We conjecture that the XAI process can be broadly disentangled into two parts, as depicted in Figure 1. The first part is uncovering what is truly happening in the model that we want to understand, while the second part is about conveying that information to the user in a consumable way. The first part is relatively easy to formalize as it mainly deals with analyzing how well a simple proxy model might generalize either locally or globally with respect to (w.r.t.) data that is generated using the black-box model. Rather than having generalization guarantees w.r.t. the underlying distribution, we now want them w.r.t. the (conditional) output distribution of the model. Once we have some way of figuring out what is truly important, a second step is to communicate this information. This second part is much less clear as we do not have an objective way of characterizing an individual’s mind. This part, we believe, is what makes explainability as a whole so challenging to formalize. A mainstay for a lot of XAI research over the last year or so has been to conduct user studies to evaluate new XAI methods.

#overviews #ai #explainability #explainable ai #xai

1626856899

One the most fundamental concepts in Probability, Statistics and Bayesian Statistics is Conditional Probability. In this StatQuest, we walk you through what Conditional Probabilities are and how to calculate them using raw data as well as unconditional probabilities. BAM!!!

Subscribe: https://www.youtube.com/c/joshstarmer/featured

#machine-learning

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*Data Scientists are modern-day statisticians that take a shot on complex business problems and unravel them with the assistance of data. Probability Distributions resemble microscope. They allow a Data Scientist or Data Analyst to recognize patterns in any case totally random variables.*

A normal distribution is generally described to as the bell-shaped curve and it depicts the recurrence of something that you are evaluating, such as the class scores. The focal point of the bend is the mean and the curve width called the standard deviation. The more extensive the curve, the more the discrepancy. The score happens most every now and again is the mean. Scores farther away from the mean become less repeated.

The normal distribution applies to numerous circumstances where the varieties in the measure are because of a bunch of reasons for example the scores can change because of contrasts in study time, IQ, school quality.

Another instance takes some sand in your hand. Drop it gradually to the ground. What do you see? A little slope like structure which resembles a normal distribution. Most of the sand will, in general, be in the centre and there are two extremities as well. This inclination to be in the centre is a central tendency.

Along these lines, the main thing you should remember is as the size of the sample increases everything starts to normal.

Normal distribution where the most likely thing is in the middle and you never need to stress about the time where the things are going on.

Exponential random variables are regularly utilized to model waiting times between events. In this way, for example, one student went to the Help Room and had a stopwatch and monitored the times when students would show to the centre for help. The distribution of these times looked near that of an exponential distribution. Another case is the number of hits a site gets in 60 min.

Suspicion for the exponential distribution that occasions happens autonomously at irregular occasions at a steady normal rate. The time between progressive occasions at that point has an exponential distribution.

An assumption for the exponential distribution that events happen independently at random times at a constant average rate. The time between consecutive events known as an exponential distribution.

#machine-learning #data-science #probability-distributions #probability

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Let’s understand why an explainable AI is making lot of fuss nowadays. Consider an example a person(consumer) Mr. X goes to bank for a personal loan and bank takes his demographic details, credit bureau details and last 6 month bank statement. After taking all the documents bank runs this on their production deployed machine Learning Model for checking whether this person will default on loan or not.

A complex ML model which is deployed on their production says that this person has 55% chances of getting default on his loan and subsequently bank rejects Mr. X personal loan application.

Now Mr X is very angry and puzzled about his application rejection. So he went to bank manager for the explanation why his personal loan application got rejected. He looks his application and got puzzled that his application is good for granting a loan but why model has predicted false. This chaos has created doubt in manager’s mind about each loan that was previously rejected by the machine learning model. Although accuracy of the model is more than 98% percentage. But still it fails to gain the trust.

Every data scientist wants to deploy model on production which has highest accuracy in prediction of output. Below is the graph shown between interpretation and accuracy of the model.

Interpreability Vs Accuracy of the Model

If you notice the increasing the accuracy of the model the interpreability of the model decrease significantly and that obstructs complex model to be used in production.

This is where Explainable AI rescue us. In Explainable AI does not only predict the outcome it also explain the process and features included to reach at the conclusion. Isn’t great right that model is explaining itself.

ML and AI application has reached to almost in each industry like Banking & Finance, Healthcare, Manufacturing, E commerce, etc. But still people are afraid to use the complex model in their field just because of they think that the complex machine learning model are black box and will not able to explain the output to businesses and stakeholders. I hope until now you have understood why Explainable AI is required for better and efficient use of machine learning and deep learning models.

**Now, Let’s understand what is Explainable AI and How does it works ?**

Explainable AI is set of tools and methods in Artificial Intelligence (AI) to explain the model output process that how an model has reached to particular output for a given data points.

Consider the above example where Mr. X loan has rejected and Bank Manager is not able to figure out why his application got rejected.Here an explainable can give the important features and their importance considered by the model to reach at this output. So now Manager has his report,

- He has more confidence on the model and it’s output.
- He can use more complex model as he is able to explain the output of the model to business and stakeholders.
- Now Mr. X got an explanation from bank about their loan rejection. He exactly knows what needs to be improved in order to get loan from the banks

#explainable-ai #explainability #artificial-intelligence #machine-learning-ai #machine-learning #deep learning

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*With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have.*

Machine learning model development is hard, especially in the real world.

Typically, you need to:

- understand the business problem,
- gather the data,
- explore it,
- set up a proper validation scheme,
- implement models and tune parameters,
- deploy them in a way that makes sense for the business,
- inspect model results only to find out new problems that you have to deal with.

And that is not all.

You should have the **experiments** you run and **models** you train **versioned** in case you or anyone else needs to inspect them or reproduce the results in the future. From my experience, this moment comes when you least expect it and the feeling of “I wish I had thought about it before” is so very real (and painful).

But there is even more.

#2020 aug tutorials # overviews #explainability #explainable ai #interpretability #python #shap