Explaining Your Machine Learning Models with SHAP and LIME! Helping you to demystify what some people might perceive as a “black box” for your machine learning models.
Helping you to demystify what some people might perceive as a “black box” for your machine learning models
Hello there all! Welcome back again to another data science quick tip. This particular post is most interesting for me not only because this is the most complex subject we’ve tackled to date, but it’s also one that I just spent the last few hours learning myself. And of course, what better way to learn than to figure out how to teach it to the masses?
Before getting into it, I’ve uploaded all the work shown in this post to a singular Jupyter notebook. You can find it at my personal GitHub if you’d like to follow along more closely.
So even though this is a very complex topic behind the scenes, I’m going to intentionally dial it down as much as possible for the widest possible audience. Even though this is ultimately a post designed for data science practitioners, I think it’s equally important for any business person to understand why they should care about this topic.
Prior to jumping into how to calculate/visualize these values, let’s build some intuition on why would we would even care about this topic.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
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7 Types of Data Bias in Machine Learning. Data bias can occur in a range of areas, from human reporting and selection bias to algorithmic and interpretation bias.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.