Mlearning.ai

FEBRUARY 15, 2023

K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it. The method seeks the k nearest neighbours among the training documents to classify a new document and uses the categories of the k nearest neighbours to weight the category candidates [3].

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Data Science Dojo

JULY 15, 2024

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. K-Nearest Neighbors (KNN): This method classifies a data point based on the majority class of its K nearest neighbors in the training data.

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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.,

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

Pickl AI

JULY 26, 2023

K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance. A smaller k implies the model is influenced by a limited number of neighbours, causing predictions to be more sensitive to noise in the training data.

Machine Learning 52

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Pickl AI

NOVEMBER 18, 2024

spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines. customer segmentation), clustering algorithms like K-means or hierarchical clustering might be appropriate. K-Nearest Neighbors), while others can handle large datasets efficiently (e.g.,

Machine Learning 52

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Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

Trade-off Of Bias And Variance: So, as we know that bias and variance, both are errors in machine learning models, it is very essential that any machine learning model has low variance as well as a low bias so that it can achieve good performance. Another example can be the algorithm of a support vector machine.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52