# Tag Info

Accepted

### What is "conditioning" on a feature?

This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the ...
• 9,257
Accepted

### Why isn't conditional probability sufficient to describe causality?

Perhaps the shortest answer to this question is that Bayes' Theorem itself allows us to easily change the direction of a conditional probability: $$P(A|B) = \frac{P(B|A)P(A)}{P(B)}$$ So if you ...
• 181

### Why isn't conditional probability sufficient to describe causality?

But the conditional probability is clearly not symmetric and captures directed relationships. One needs to consider the kinds of directed relationships that is captured by conditional probability. It ...
• 699

### Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?

Conditional probability and Bayes rule are related but they are not the same thing, you can predict conditional probabilities without using Bayes rule. So no, not all machine learning classifiers use ...
• 1,355
Accepted

### Solving the supervised learning problem of learning $p(y \vert \mathbf{x})$ by using traditional unsupervised technologies to learn $p(\mathbf{x}, y)$

This is the definition of conditional probability + Total probability decomposition formula: $p(y|x) = \frac{p(y,x}{p(x)} = \frac{p(x,y)}{\sum_{y'}p(x,y')}$. The idea is to use some unsupervised ...
• 381
The relationship between the axes of graph (1) and your variables $x$ and $y$ is not clear, so this generalized answer may be helpful or useless. From graph (1) it appears that the correlation ...