# Tag : Gentle

AI/ML

## A Gentle Introduction to the Fbeta-Measure for Machine Learning

Fbeta-measure is a configurable single-score metric for evaluating a binary classification model based on the predictions made for the positive class. The Fbeta-measure is calculated...
AI/ML

## A Gentle Introduction to Threshold-Moving for Imbalanced Classification

Classification predictive modeling typically involves predicting a class label. Nevertheless, many machine learning algorithms are capable of predicting a probability or scoring of class membership,...
AI/ML

## A Gentle Introduction to Probability Metrics for Imbalanced Classification

Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of class membership. For these problems,...
AI/ML

## A Gentle Introduction to Imbalanced Classification

Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the...
AI/ML

## A Gentle Introduction to the Bayes Optimal Classifier

The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is described using the Bayes Theorem...
AI/ML

## A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset....
AI/ML

## A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is...
AI/ML

## A Gentle Introduction to Expectation-Maximization (EM Algorithm)

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and...
AI/ML

## A Gentle Introduction to Monte Carlo Sampling for Probability

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where describing or estimating the probability...
AI/ML

## A Gentle Introduction to Markov Chain Monte Carlo for Probability

Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable with probabilistic models, and instead,...
AI/ML

## A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation

Logistic regression is a model for binary classification predictive modeling. The parameters of a logistic regression model can be estimated by the probabilistic framework called...
AI/ML

## A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation

Linear regression is a classical model for predicting a numerical quantity. The parameters of a linear regression model can be estimated using a least squares...
AI/ML

## A Gentle Introduction to Bayesian Belief Networks

Probabilistic models can define relationships between variables and be used to calculate probabilities. For example, fully conditional models may require an enormous amount of data...
AI/ML

## A Gentle Introduction to Information Entropy

Information theory is a subfield of mathematics concerned with transmitting data across a noisy channel. A cornerstone of information theory is the idea of quantifying...
AI/ML

## A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving...
AI/ML

## A Gentle Introduction to Cross-Entropy for Machine Learning

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and...
AI/ML

## A Gentle Introduction to Joint, Marginal, and Conditional Probability

Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the probability for a single variable....
AI/ML

## A Gentle Introduction to Probability Density Estimation

Probability density is the relationship between observations and their probability. Some outcomes of a random variable will have low probability density and other outcomes will...
AI/ML

## A Gentle Introduction to Probability Distributions

Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all possible outcomes. A thing of...
AI/ML

## A Gentle Introduction to Uncertainty in Machine Learning

Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the...

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