filmov
tv
Become an AI Pro: Mastering LIME in Python Made Easy
Показать описание
To access my secret discount portal:
Dataset to use in the video:
Local surrogate models are interpretable models that are used to explain individual predictions of black-box machine learning models. Local interpretable model-agnostic explanations (LIME) is a paper in which the authors propose a concrete implementation of local surrogate models. Surrogate models are trained to approximate the predictions of the underlying black-box model. Instead of training a global surrogate model, LIME focuses on training local surrogate models to explain individual predictions.
The idea is quite intuitive. First, forget about the training data and imagine you only have the black-box model where you can input data points and get the model's predictions. You can probe the box as often as you want. Your goal is to understand why the machine learning model made a specific prediction. LIME tests what happens to the predictions when you give variations of your data into the machine learning model. LIME generates a new dataset consisting of permuted samples and the corresponding predictions of the black-box model.
Intro(0:00)
Import libraries(0:56)
Loading Data(1:55)
Data processing(4:43)
Random Forest(8:36)
Lime(9:48)
Outro(16:34)
Dataset to use in the video:
Local surrogate models are interpretable models that are used to explain individual predictions of black-box machine learning models. Local interpretable model-agnostic explanations (LIME) is a paper in which the authors propose a concrete implementation of local surrogate models. Surrogate models are trained to approximate the predictions of the underlying black-box model. Instead of training a global surrogate model, LIME focuses on training local surrogate models to explain individual predictions.
The idea is quite intuitive. First, forget about the training data and imagine you only have the black-box model where you can input data points and get the model's predictions. You can probe the box as often as you want. Your goal is to understand why the machine learning model made a specific prediction. LIME tests what happens to the predictions when you give variations of your data into the machine learning model. LIME generates a new dataset consisting of permuted samples and the corresponding predictions of the black-box model.
Intro(0:00)
Import libraries(0:56)
Loading Data(1:55)
Data processing(4:43)
Random Forest(8:36)
Lime(9:48)
Outro(16:34)
0:16:28
PRO Mastering Engineer VS AI
0:23:10
We Proved It: AI Mastering Is A Waste Of Money
0:16:57
Become an AI Pro: Mastering LIME in Python Made Easy
0:06:43
I Tried 6 AI Mastering Services & Found My Favourite
0:11:56
I Paid 5 Mastering Engineers to Master the Same Song... The Result is SHOCKING
0:09:51
A.I. Mastering VS Professional Engineer
21:11:21
Generative AI for Developers – Comprehensive Course
0:00:48
Andrew Ng's Secret to Mastering Machine Learning - Part 1 #shorts
0:00:54
Mastering ShadCN: Build Websites Like a Pro!
0:09:02
This is why I don't need a mastering engineer!
0:08:53
Logic's AI Mastering Assistant EXPLAINED | 5-Minute Logic Expert (Pt 30)
0:00:51
Is AI MASTERING any GOOD? 🤖🎧
0:01:01
5 Pro Tips for Mastering ChatGPT: Become an AI Language Pro in no time! #ChatGPT #AI #openai
0:08:52
The Logic Pro 10.8 Update Now has A.I. Mastering!! 🤯
0:00:22
There's AI Mastering in Your DAW Now? #musicproduction #ai #musician
0:09:24
Mastering a Mix Online? YES, In Under 3 Minutes
0:07:09
AI Mastering Vs. Mastering Engineer? Landr Review
0:16:02
Mastering Python - Everything You Need To Know To Become a Python Master
0:00:54
AI vs Human Mastering - Which One Is Better?
0:04:26
Mastering a Song with LANDR Mastering Plugin
0:14:01
Artificial Intelligence in Mastering | Are You Listening? Season 3, Episode 5
0:23:00
Stop Ruining Your MASTERS TRY THIS | LANDR Mastering Plugin
0:00:58
Pro Mastering in 4 Simple Steps
0:00:49
Can you tell the difference between a human mastering engineer (@whiteseastudio), and AI mastering?
Комментарии