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Data mining

Dataconomy

Classification Classification techniques, including decision trees, categorize data into predefined classes. Clustering Clustering groups similar data points based on their attributes. One common example is k-means clustering, which segments data into distinct groups for analysis.

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Predictive modeling

Dataconomy

Definition and overview of predictive modeling At its core, predictive modeling involves creating a model using historical data that can predict future events. Unsupervised models Unsupervised models typically use traditional statistical methods such as logistic regression, time series analysis, and decision trees.

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Decision boundary

Dataconomy

The decision boundary would be a line that separates these two groups, determining whether a new point falls into the cat or dog category based on its features. Definition of decision boundary The definition of a decision boundary is rooted in its functionality within classification algorithms.

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Supervised learning

Dataconomy

Definition of supervised learning At its core, supervised learning utilizes labeled data to inform a machine learning model. Common algorithms used in classification tasks include: Decision Trees: A tree-like model that makes decisions based on feature values.

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Machine learning algorithms

Dataconomy

Machine learning algorithms are specialized computational models designed to analyze data, recognize patterns, and make informed predictions or decisions. Definition and importance of machine learning algorithms The core value of machine learning algorithms lies in their capacity to process and analyze vast amounts of data efficiently.

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Discover the Role of Entropy in Machine Learning

Pickl AI

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Entropy enhances clustering, federated learning, finance, and bioinformatics.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

It helps business owners and decision-makers choose the right technique based on the type of data they have and the outcome they want to achieve. Let us now look at the key differences starting with their definitions and the type of data they use. In this case, every data point has both input and output values already defined.