# Tag Info

Accepted

### Trying to extrapolate info from a partial data set - statistical inference

What you are describing is commonly called conditional probability. In other words, the probability of an event occurring, given that another event has occurred. Bayes' theorem is a way of conducting ...
1 vote

### Precision vs probability

Precision is usually defined as a quality metric and describes (in binary classification) the fraction of correctly classified instances among all instances that it classified as True. There is a ...
1 vote
Accepted

### Predicting probability of reaching a milestone -- How much data should I use from production universe to train/test model?

You can use 100% of the data. The more data you feed to the model, the more accurate the prediction is going to be. If you have only two features - time and live production data, 30k records is not ...
1 vote

### Probability Distributions

Probability and statistics form the basis for a lot of the classical Machine Learning algorithms. Apart from that, a Data Scientist or a Statistician uses probability directly in the following areas: ...
1 vote
Accepted

### What does maximize average log probability mean?

This formula does not mazimize anything. You have to maximize it, usually with gradient descent.
1 vote

### Is probabilities mean of predicted class (RandomForest) a consistent estimator of class recall?

Unfortunately this doesn't work. To understand why, let's think about the perfect classifier and the maximally wrong classifier. The perfect classifier has 100% recall on any given class. If there are ...
1 vote

### Edit friendly DDPM noise space

Equation 2 refers to creating $x_t$ from $x_0$. In the proposed method, each $x_t$ was generated from $x_0$ by sampling a random noise, i.e.: \$\\x_1 = \sqrt{\bar{\alpha_1}}\cdot x_0 + \sqrt(1-\bar{\...
1 vote
Accepted

### Do models of social systems suffer from prediction drift?

I wouldn't call it normal but it surely is possible. There are several reasons: Changes in fraud patterns: If the model was trained on historical data that is no longer representative of current ...

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