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 244

K-nearest Neighbors Support Vector Machines Machine Learning Machine Learning 244

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

KDnuggets

DECEMBER 10, 2019

From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.

K-nearest Neighbors 294

K-nearest Neighbors Data Preparation Python Machine Learning 294

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. Data visualization charts and plot graphs can be used for this.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. K-Nearest Neighbors), while others can handle large datasets efficiently (e.g., Key Takeaways Machine Learning Models are vital for modern technology applications.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

PyImageSearch

SEPTEMBER 16, 2024

Similarly, autoencoders can be trained to reconstruct input data, and data points with high reconstruction errors can be flagged as anomalies. Proximity-Based Methods Proximity-based methods can detect anomalies based on the distance between data points. We will start by setting up libraries and data preparation.

Clustering 56

Clustering Algorithm Machine Learning Machine Learning 56

AWS Machine Learning Blog

SEPTEMBER 25, 2024

Solution overview In this solution, we start with data preparation, where the raw datasets can be stored in an Amazon Simple Storage Service (Amazon S3) bucket. We provide a Jupyter notebook to preprocess the raw data and use the Amazon Titan Multimodal Embeddings model to convert the image and text into embedding vectors.

AWS 83

AWS K-nearest Neighbors ML ML 83