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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

Flipboard

Embedded methods : Perform feature selection during model training using techniques like Lasso (L1 regularization) or decision tree feature importance. Document Everything : Keep clear and versioned documentation of how each feature is created, transformed, and validated. recursive feature elimination).

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Exploring All Types of Machine Learning Algorithms

Pickl AI

Key examples include Linear Regression for predicting prices, Logistic Regression for classification tasks, and Decision Trees for decision-making. Decision Trees visualize decision-making processes for better understanding. Linear Regression predicts continuous outcomes, like housing prices.

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Clustering in machine learning

Dataconomy

Segmentation for model enhancement: Cluster information often improves the performance of supervised learning models like regression and decision trees. Document categorization: Clustering can help organize large collections of documents based on content similarity.

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Multi-class classification

Dataconomy

This is typical in situations where an image or a document may belong to several categories, such as tagging a photo with different attributes like beach, sunset, and family. Decision trees Decision trees represent a simple yet powerful algorithm for multi-class classification.

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Neuro-symbolic AI

Dataconomy

Symbolic approaches, such as decision trees, offer clarity and reasoning but may lack the speed and capacity of neural networks. Intelligent documents: Automating the analysis of documents improves information retrieval and management. However, they can struggle with interpretability.

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Ask HN: What Are You Working On? (June 2025)

Hacker News

Do you know if the FPGA and/or hardware communities use any type of formalism for design or documentation of state machines? Subscribers, ahem secret agents, receive packages every few weeks containing reproductions of famous documents, stanps from the USSR, Cuba, Czechoslovakia, coins, and other fun stuff.

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How to build a decision tree model in IBM Db2

IBM Journey to AI blog

In this post, I will show how to develop, deploy, and use a decision tree model in a Db2 database. Using examples from the dataset, we’ll build a classification model with decision tree algorithm. Since I will create a decision tree model, I don’t need to deal with the large value and the missing values.