I want know that how the confidence score is calculated for chat bots ?
It Totally depends on the pipeline that you are using in config.yml. You need to dig into each and every component for that. Rasa Components
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I am seeing on the LUIS documentation page here that you absolutely recommend to treat Data Imbalance (e.g. the differing number of total unterances compared amongst various intents) as a first priority. We currently see a mean of 19 Utterances per Intent on our dashboard, so in my opinion I should optimize all Intents towards having about 20 Utterances each as an example.
Now my question: When I use active learning by adding Endpoint Utterances, Utterances will be added to the intent we see them fitting (Active Learning Documentation). How can I ensure, that the number of utterances per intent will always remain equal (e.g. around 20 in our example)? In my opinion naturally by attributing endpoint utterances to Intents, a Data Imbalance will be created again.
Thanks a lot!
Best,
Mark
After your initial model is satisfactory, there no longer needs to be equality between intents, active learning specifically tries to correct for cases that were unseen of before, so if other examples already cover all your cases, then you don’t need to actively correct it.
when I am using LUIS to create intents, I get a 0.53.
when I added one question, it changes to 0.82.
But when I remove the question, the scores did not go back to 0.53, but to 0.62.
Is it normal for LUIS to act like this?
Scores are not absolute, and only have meaning relative to other scores in the same request.
LUIS training is nondeterministic, so between versions, and even between exporting and reimporting the exact same version of the app, an application and its models will not necesssarily return the exact same scores.
Your system should use the highest scoring intent regardless of its value. For example, a score below 0.5 does not necessarily mean that LUIS has low confidence. Providing more training data can help increase the score of the most-likely intent.
In "look up metrics” I’m trying to know how my players improve in playing my game.
I have the score (both as desing event and progression, just to try) and in look up metrics I try to “filter” with session number or days since install but, even if I group by Dimension, this doesn’t produce any result.
For instance if I do the same but with device filter it shows me the right histogram with score's mean per device.
What am I doing wrong?
From the customer care:
The session filter works only on core metrics at this point (like DAU). We hope to make this filter compatible with custom metrics as well but this might take time as we first need to include this improvement to our roadmap and then evaluate it by comparing it with our other tasks. As a result, there is no ETA on making a release.
I would recommend you to download the raw data (go to "Export data" in the settings of the game) and perform an analysis on your own for this sort of "per user" analysis. You should be able to create stats per user. GA does not do this since your game can reach millions of users and there's no way you can plot this amount of entries in a browser.
I was using AlchemyAPI for text analysis. I want to know if there is way to influence the API results or fine-tune it as per the requirement.
I was trying to analyse different call center conversations available on internet. To understand the sentiments i.e. whether customer was unsatisfied/angry and hence conversation is negative.
For 9 out of 10 conversations it gave sentiment as positive and for 1 it was negative. That conversation was about emergency response system (#911 in US). It seems that words shooting, fear, panic, police, siren could have cause this result.
But actually the whole conversation was fruitful. Caller was not angry with the service instead call center person solved the caller's problem and caller was relaxed. So logically this should not be treated as negative.
What is the way ahead to customize the AlchemyAPI behavior ?
We are currently looking at the tools that would be required to allow customization of the AlchemyAPI services. Our current service is entirely pre-trained on billions of web pages, but customization is on the road map. I can't give you any timelines this early, but keep checking back!
Zach, Dev Evangelist AlchemyAPI
I am implementing a web application that has many users and I would give the users rating based on their activities and based on other users liking their activities. How would I implement such an algorithm for that? I am looking for elegant and smart algorithm that could help.
You are basically looking for Scoring Algos. These articles might help -
How not to sort by average rating
Rank hotness with Newtons law of Cooling
How Reddit Ranking Algorithms work
Hope this helps.
Maybe your answer is staring right at you next to your username on this site :-) Stackoverflow.com's scoring system and badges are here to promote certain behaviors on the site. The algorithm is simple and the feedback is immediate so that everybody can see the consequences of certain actions.
What are the ratings used for? If you want to use the ratings as incentives for you users to encourage a specific behavior, then I believe you need to look at disciplines like behavioral psychology to figure out what behaviors you want to measure and reward.
If you already have a user base that reflects the typical user base you're trying to address, you might want to try with simple trial and error. Pick some actions, like e.g. receiving a like on a post and add points to the user's score whenever that happens. Watch the user community's reaction when you introduce the scoring system and see it it helps motivate the behavior you want. If not, try to change some other parameters and repeat.
Depending on your system, some users might try to game the system, so you could find yourself locked into an eternal cat and mouse game once you introduce a rating system (example: Google page ranking).