Data Science Dojo

FEBRUARY 14, 2024

Normalization A feature scaling technique is often applied as part of data preparation for machine learning.

K-nearest Neighbors 243

K-nearest Neighbors Support Vector Machines Machine Learning Machine Learning 243

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

Towards AI

JULY 19, 2023

Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. For example, linear regression is typically used to predict continuous variables, while decision trees are great for classification and regression tasks. Random Forests).

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

The MLOps Blog

DECEMBER 19, 2022

Lesson 1: Mitigating data sparsity problems within ML classification algorithms What are the most popular algorithms used to solve a multi-class classification problem? index.add(xb) # xq are query vectors, for which we need to search in xb to find the k nearest neighbors. # Creating the index.

ML 52

ML ML Algorithm Deep Learning 52