Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

K-nearest Neighbors 80

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 80

Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

Machine Learning 102

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 102

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. The feature space reduction is performed by aggregating clusters of features of balanced size. This clustering is usually performed using hierarchical clustering.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Often, these trees adhere to an elementary if/then structure.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Often, these trees adhere to an elementary if/then structure.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Mlearning.ai

FEBRUARY 17, 2023

Simple linear regression Multiple linear regression Polynomial regression Decision Tree regression Support Vector regression Random Forest regression Classification is a technique to predict a category. The most common unsupervised algorithms are clustering, dimensionality reduction, and association rule mining.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. “Means,” or average data, refers to the points in the center of the cluster that all other data is related to.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Pickl AI

DECEMBER 9, 2024

In contrast, decision trees assume data can be split into homogeneous groups through feature thresholds. Every Machine Learning algorithm, whether a decision tree, support vector machine, or deep neural network, inherently favours certain solutions over others.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

Pickl AI

SEPTEMBER 3, 2023

Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Points that don’t belong to any cluster or are in low-density regions are considered anomalies.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

NOVEMBER 18, 2024

Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. Decision trees are easy to interpret but prone to overfitting. Different algorithms are suited to different tasks.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

The MLOps Blog

DECEMBER 19, 2022

1 KNN 2 Decision Tree 3 Random Forest 4 Naive Bayes 5 Deep Learning using Cross Entropy Loss To some extent, Logistic Regression and SVM can also be leveraged to solve a multi-class classification problem by fitting multiple binary classifiers using a one-vs-all or one-vs-one strategy. A set of classes sometimes forms a group/cluster.

ML 52

ML ML Algorithm Deep Learning 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

DagsHub

DECEMBER 23, 2024

This allows it to evaluate and find relationships between the data points which is essential for clustering.  They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN).

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52