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

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 79

K-nearest Neighbors Algorithm Clustering Support Vector Machines 79

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 Decision Trees Algorithm 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 91

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 91

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

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

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