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

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

ODSC - Open Data Science

JANUARY 5, 2024

It is easy to use, with a well-documented API and a wide range of tutorials and examples available. First, it’s easy to use, the code is easy to learn and it has a well-documented API. Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use. What really makes Django are a few things.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Mlearning.ai

FEBRUARY 15, 2023

Figure 5 Feature Extraction and Evaluation Because most classifiers and learning algorithms require numerical feature vectors with a fixed size rather than raw text documents with variable length, they cannot analyse the text documents in their original form.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Pickl AI

SEPTEMBER 12, 2024

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. K K-Means Clustering: An unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

DagsHub

DECEMBER 23, 2024

  Image classification Text categorization Document sorting Sentiment analysis Medical image diagnosis Advantages Pool-based active learning can leverage relationships between data points through techniques like density-based sampling and cluster analysis.   Traditional Active Learning has the following characteristics.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

Dataconomy

DECEMBER 12, 2024

These complex data formats are usually unstructured, structurally only a set of bytes in a given field, about which the user often has no reliable information due to incomplete documentation. Overfitting can occur when the model uses too many features, causing it to make decisions faster, for example, at the endpoints of decision trees.

K-nearest Neighbors 91

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 91