Dataconomy

MARCH 28, 2025

Their application spans a wide array of tasks, from categorizing information to predicting future trends, making them an essential component of modern artificial intelligence. Machine learning algorithms are specialized computational models designed to analyze data, recognize patterns, and make informed predictions or decisions.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Towards AI

MAY 10, 2024

There are various techniques used to obtain information about tumors. Correlation: gives information about how correlated a pixel is to its neighboring pixels. The three weak learner models used for this implementation were k-nearest neighbors, decision trees, and naive Bayes.

K-nearest Neighbors 105

K-nearest Neighbors Decision Trees Machine Learning Machine Learning 105

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

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

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code.

Machine Learning 116

Machine Learning Machine Learning Algorithm K-nearest Neighbors 116

Towards AI

APRIL 14, 2023

Adding such extra information should improve the classification compared to the previous method (Principle Label Space Transformation). The prediction is then done using a k-nearest neighbor method within the embedding space. Distance preserving embeddings: The name of this method is straightforward.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Pickl AI

JULY 31, 2023

Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction. Instance Similarity : Lazy Learning algorithms use a similarity measure (e.g.,

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Mlearning.ai

FEBRUARY 2, 2023

In the context of the MAB algorithm, each arm represents a decision that can be taken, and the reward corresponds to some measure of performance or utility. bag of words or TF-IDF vectors) and splitting the data into training and testing sets.

K-nearest Neighbors 52

K-nearest Neighbors Algorithm Support Vector Machines Decision Trees 52

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. It’s a fantastic world, trust me! You can also look at my GitHub portfolio to see the actual applications of some of them.

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. These equations use labeled and unlabeled data to predict future outcomes when only some of the information is known.

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. Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

Mlearning.ai

NOVEMBER 29, 2023

K-Nearest Neighbor Regression Neural Network (KNN) The k-nearest neighbor (k-NN) algorithm is one of the most popular non-parametric approaches used for classification, and it has been extended to regression. Decision Trees ML-based decision trees are used to classify items (products) in the database.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

SEPTEMBER 12, 2024

By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success. Data Science is the art and science of extracting valuable information from data.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

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. predicting house prices), Linear Regression, Decision Trees, or Random Forests could be good choices.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Pickl AI

SEPTEMBER 3, 2023

In many fields, finding anomalies can yield insightful data and useful information. An ensemble of decision trees is trained on both normal and anomalous data. k-Nearest Neighbors (k-NN): In the supervised approach, k-NN assigns labels to instances based on their k-nearest neighbours.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

JULY 26, 2023

Here are some examples of variance in machine learning: Overfitting in Decision Trees Decision trees can exhibit high variance if they are allowed to grow too deep, capturing noise and outliers in the training data. Carefully selecting informative features can reduce bias by focusing on relevant patterns.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Mlearning.ai

FEBRUARY 15, 2023

Some important things that were considered during these selections were: Random Forest : The ultimate feature importance in a Random forest is the average of all decision tree feature importance. A random forest is an ensemble classifier that makes predictions using a variety of decision trees. Dönicke, T., Manning C.

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Mlearning.ai

MAY 23, 2023

Data analytics deals with checking the existing hypothesis and information and answering questions for a better and more effective business-related decision-making process. Decision trees are more prone to overfitting. Some algorithms that have low bias are Decision Trees, SVM, etc. Let us see some examples.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

DagsHub

DECEMBER 23, 2024

We must understand that not all the data samples contribute to providing valuable information. Faster Learning Curve Active Learning achieves better model performance with fewer labeled examples by focusing on the most informative cases. But why is this an important and valuable approach? Reason, presence of redundant samples.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 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. . Creating the index.

ML 52

ML ML Algorithm Deep Learning 52

Dataconomy

SEPTEMBER 1, 2023

By combining different techniques, such as feature selection, feature extraction, and feature transformation, hybrid machine learning techniques can help identify the most informative features that contribute to effective heart disease prediction. The decision tree algorithm used to select features is called the C4.5

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195

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. Without meta-information it is difficult to draw conclusions about the type of content and its interpretation.

K-nearest Neighbors 91

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