Articles
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91 results found
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Logistic regression (machine learning)
Logistic regression in machine learning is a classification model which predicts the probabilities of binary outcomes, as opposed to linear regression which predicts actual values.
Logistic regression outputs are constrained between 0 and 1, and hence is a popular simple classification method ...
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Decision tree (machine learning)
The decision tree model in machine learning is an algorithm that offers choices based on characteristics of the data. It follows 'branch node theory' in which each branch will represent a variable alongside a decision.
Often decision tree models will be expressed in the following rule format: ...
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Linear regression (machine learning)
Linear regression in machine learning is a form of supervised learning, derived from the linear regression models in statistics. It operates under the assumption that two variables have a linear relationship, therefore, can calculate the value of an output variable based on the input variable. L...
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Confusion matrix
Confusion matrices, a key tool to evaluate machine learning algorithm performance in classification, are a statistical tool.
Contingency tables, a type of confusion matrix, are used in the evaluation of many diagnostic exams for sensitivity, specificity, positive and negative predictive values....
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Feature scaling
Feature scaling a preprocessing technique that is used to standardize the range of values in data features, making sure that the features are on a similar scale. It is used when the range of values of a certain feature is too variable and contains extreme values as most algorithms perform poorly...
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Cost function (machine learning)
A cost function is a mechanism utilized in supervised machine learning, the cost function returns the error between predicted outcomes compared with the actual outcomes. The aim of supervised machine learning is to minimize the overall cost, thus optimizing the correlation of the model to the sy...
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Iteration (machine learning)
An iteration is a term used in machine learning and indicates the number of times the algorithm's parameters are updated. Exactly what this means will be context dependent. A typical example of a single iteration of training of a neural network would include the following steps:
processing the ...
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Batch size (machine learning)
Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration. The batch size can be one of three options:
batch mode: where the batch size is equal to the total dataset thus making the iteration and epoch values equivalent
mini-batch mod...
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Bagging
Bagging is a term often used in the fields of machine learning, data science and computational statistics that refers to bootstrap aggregation. Bootstrapped aggregation of data can be employed in many different AI (artificial intelligence) algorithms, and is often a necessary step to making rand...
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Reinforcement learning (machine learning)
Reinforcement learning is one of the main algorithms used in machine learning in the context of an agent in an environment. In each timestep, this agent takes in information from their environment and performs an action. Certain actions reward the agent.
Reinforcement learning maximizes these ...
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Rule-based expert systems
A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem 1. The aim of the expert system is to take knowledge from a human expert and convert this into a number of hardcoded rules to apply to the input data. In their...
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