Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in […]

# Archive | Probability

## 5 Reasons to Learn Probability for Machine Learning

Probability is a field of mathematics that quantifies uncertainty. It is undeniably a pillar of the field of machine learning, […]

## Resources for Getting Started With Probability in Machine Learning

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and […]

## How to Develop and Evaluate Naive Classifier Strategies Using Probability

A Naive Classifier is a simple classification model that assumes little to nothing about the problem and the performance of […]

## A Gentle Introduction to Jensen’s Inequality

It is common in statistics and machine learning to create a linear transform or mapping of a variable. An example […]

## A Gentle Introduction to Probability Scoring Methods in Python

How to Score Probability Predictions in Python and Develop an Intuition for Different Metrics. Predicting probabilities instead of class labels […]

## How and When to Use a Calibrated Classification Model with scikit-learn

Instead of predicting class values directly for a classification problem, it can be convenient to predict the probability of an […]

## How to Use ROC Curves and Precision-Recall Curves for Classification in Python

It can be more flexible to predict probabilities of an observation belonging to each class in a classification problem rather […]

## Do Not Use Random Guessing As Your Baseline Classifier

I recently received the following question via email: Hi Jason, quick question. A case of class imbalance: 90 cases of […]