Towards AI

MARCH 17, 2024

First bringing together conflicting literature on what XAI is and some important definitions and distinctions. The current state of explainability … explained Any research on explainability will show that there is little by way of a concrete definition. Interpretability — Explaining the meaning of a model/model decisions to humans.

AI 105

AI AI Decision Trees Artificial Intelligence 105

Towards AI

SEPTEMBER 10, 2023

This one is definitely one of the most practical and inspiring. So you definitely can trust his expertise in Machine Learning and Deep Learning. So you definitely can trust his expertise in Machine Learning and Deep Learning. I’ve passed many ML courses before, so that I can compare. About the course The Fast.AI

Deep Learning 124

Deep Learning Deep Learning ML ML 124

Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Lets delve into its mathematical definition and key properties.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

Smart Data Collective

JUNE 7, 2019

For centuries before the existence of computers, humans have imagined intelligent machines that were capable of making decisions autonomously. At the early era of Artificial Intelligence, programmers tried to teach machines from the definition of logical rules that the machine itself could extend during the execution of the program.

Machine Learning 101

Machine Learning Machine Learning Supervised Learning Support Vector Machines 101

Mlearning.ai

JULY 24, 2023

This article will cover the basics of Decision Trees and XGBoost, and demonstrate how to implement the latter for classifying celestial objects in the night sky as either a Galaxy, Star, or Quasar. Photo by Nathan Anderson on Unsplash Brief Intro to ML Algorithms A decision tree is a widely used algorithm in machine learning.

Decision Trees 52

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Towards AI

FEBRUARY 20, 2024

Definition says, machine learning is the ability of computers to learn without explicit programming. Linear Regression Decision Trees Support Vector Machines Neural Networks Clustering Algorithms (e.g., I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’.

Machine Learning 109

Machine Learning Machine Learning ML ML 109

Towards AI

APRIL 14, 2023

Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels. In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Pickl AI

APRIL 16, 2025

Decision Trees: Represent hypothesis as conditional rules. Decision Trees : Ideal for classification tasks using conditional rules. For decision trees: Conditional rules like “If income > 50K, then class = high.” Decision trees examine various tree structures.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Pickl AI

JULY 12, 2024

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Decision Trees Decision trees recursively partition data into subsets based on the most significant attribute values. classification, regression) and data characteristics.

Artificial Intelligence 52

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Dataconomy

APRIL 4, 2024

From deterministic software to AI Earlier examples of “thinking machines” included cybernetics (feedback loops like autopilots) and expert systems (decision trees for doctors). When the result is unexpected, that’s called a bug. But these were still predictable and understandable. They just followed a lot of rules.

Algorithm 103

Algorithm AI AI Decision Trees 103

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Decision trees are easy to interpret but prone to overfitting.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. , you already know that our approach in this series is math-heavy instead of code-heavy.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. AdaBoos t A formal definition of AdaBoost (Adaptive Boosting) is “the combination of the output of weak learners into a weighted sum, representing the final output.” Table 1: The Dataset.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

ODSC - Open Data Science

FEBRUARY 5, 2025

He dissected the four core elements of AI agents: task definition, tool usage, rules and constraints, and structured prompts. Ozdemir focused on structured agent workflows , breaking down AI tasks into decision trees with modular components.

AI 52

AI AI Decision Trees Machine Learning 52

Mlearning.ai

NOVEMBER 29, 2023

Decision Trees ML-based decision trees are used to classify items (products) in the database. In its core, lie gradient-boosted decision trees. For instance, when used with decision trees, it learns to outline the hardest-to-classify data instances over time. But the results should be worth it.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

MAY 23, 2023

Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Hence, we have various classification algorithms in machine learning like logistic regression, support vector machine, decision trees, Naive Bayes classifier, etc. character) is underlined or not.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Heartbeat

SEPTEMBER 13, 2023

The scholar, in her work , opines that: Interpretability is about understanding how the model works, whereas explainability involves providing justifications for specific predictions or decisions. Decision trees can be trained and visualized in rule-based explanations to reveal the underlying decision logic. Singh, S. &

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

JULY 26, 2023

In this article, we will explore the definitions, differences, and impacts of bias and variance, along with strategies to strike a balance between them to create optimal models that outperform the competition.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

ML @ CMU

MAY 12, 2023

Privacy definition: There are a small number of clients, but each holds many data subjects, and client-level DP isn’t suitable. imposing a max node degree and accounting for it), are there alternative privacy definitions that offer varying degrees of protection for varying node connectedness?

Data Silos 130

Data Silos Algorithm ML ML 130

Mlearning.ai

JANUARY 26, 2023

CIU is model-agnostic and provides uniform explanation concepts for all possible DSS models, ranging from linear models such as the weighted sum, to rule-based systems, decision trees, fuzzy systems, neural networks and any machine learning-based models. This definition of CU differs from CI by handling negative A values correctly.

AI 52

AI AI Machine Learning Decision Trees 52

AWS Machine Learning Blog

JUNE 22, 2023

The best baseline was achieved with a weighted ensemble of gradient boosted decision tree models. The ML model takes in the historical sequence of machine events and other metadata and predicts whether a machine will encounter a failure in a 6-hour future time window.

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Pickl AI

JANUARY 29, 2024

AI automates and optimises Data Science workflows, expediting analysis for strategic decision-making. Data Science Vs Machine Learning Vs AI Aspect Data Science Artificial Intelligence Machine Learning Definition Data Science is the field that deals with the extraction of knowledge and insights from data through various processes.

Data Science 52

Data Science ML ML Machine Learning 52

Pickl AI

MAY 31, 2023

Begin by employing algorithms for supervised learning such as linear regression , logistic regression, decision trees, and support vector machines. It includes regression, classification, clustering, decision trees, and more. To obtain practical expertise, run the algorithms on datasets.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

AWS Machine Learning Blog

OCTOBER 8, 2024

The following figure is a decision tree to help you decide what type of endpoint to use. The diagram outlines a decision-making process for selecting between batch, asynchronous, or real-time inference endpoints.

Machine Learning 95

Machine Learning Machine Learning Data Preparation AWS 95

Pickl AI

NOVEMBER 5, 2024

These statistics underscore the significant impact that Data Science and AI are having on our future, reshaping how we analyse data, make decisions, and interact with technology. Machine Learning Expertise Familiarity with a range of Machine Learning algorithms is crucial for Data Science practitioners.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. Definition of AI AI refers to developing computer systems that can perform tasks that require human intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Mlearning.ai

APRIL 15, 2023

Lists Definitely the most used structure, they are a set of elements separated by the comma. Trees Hierarchical structures are important for Machine Learning for the creation of Decision trees (I will talk about it soon if we are lucky). Within the images you will find the code comments.

Algorithm 52

Algorithm Python Decision Trees Computer Science 52

Pickl AI

MAY 30, 2024

It’s critical in harnessing data insights for decision-making, empowering businesses with accurate forecasts and actionable intelligence. Options include linear regression for continuous outcomes and decision trees for classification tasks. The choice impacts the model’s performance and accuracy.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Pickl AI

NOVEMBER 2, 2023

linear regression, decision trees , SVM) – Understanding about the perfect fit for using each algorithm – Parameters and hyperparameters to tune Click here to access -> Cheat sheet for Key Machine Learning Algorithms Deep Learning Concepts and Neural Network Architectures – Neural network components and their functions (e.g.,

Data Scientist 52

Data Scientist Data Science Data Visualization Machine Learning 52

Mlearning.ai

APRIL 13, 2023

This is achieved through the use of a positive definite kernel function, k(x,y), which satisfies the property: k(x,y) = <φ(x), φ(y)> where φ(x) is the mapping of the input data into the high-dimensional feature space and < ,> is the inner product in the RKHS.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Support Vector Machines 52

AssemblyAI

NOVEMBER 9, 2023

It's in contrast to a really broad and undefined definition of the word ”outcome” Paul, one of the design managers at Intercom, was struggling to differentiate between customer outcomes and business impact. High variance means overfitting models with high flexibility tend to have high variance like decision trees.

AI 102

AI AI Machine Learning Machine Learning 102

DataRobot

JUNE 17, 2021

Personally, I think there are definitely horizons in math, biology, and physics that we may never reach… but maybe AI can change that. Ten years ago, this same month, I finished my PhD and started working on Eureqa full time. This search for mathematical formulas makes Eureqa different from other machine learning algorithms.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Dataconomy

APRIL 16, 2025

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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Supervised Learning Decision Trees Algorithm Machine Learning 91

Dataconomy

MARCH 28, 2025

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.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Dataconomy

MARCH 25, 2025

Understanding baseline models The definition of baseline models emphasizes their purpose in machine learning: they define a minimum level of performance that any model must achieve to be considered useful. Decision trees: Provide interpretable predictions based on logical rules.

Decision Trees 91

Decision Trees Machine Learning Machine Learning Data Scientist 91

Dataconomy

MARCH 27, 2025

Definition and purpose Neural networks are designed to mimic human brain functions using layers of interconnected nodes, processing input data through complex mathematical computations. Symbolic approaches, such as decision trees, offer clarity and reasoning but may lack the speed and capacity of neural networks.

AI 125

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Dataconomy

MARCH 25, 2025

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.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Clustering 91