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Unlocking the Power of KNN Algorithm in Machine Learning

Pickl AI

Nevertheless, its applications across classification, regression, and anomaly detection tasks highlight its importance in modern data analytics methodologies. The K Nearest Neighbors (KNN) algorithm of machine learning stands out for its simplicity and effectiveness. What are K Nearest Neighbors in Machine Learning?

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Five machine learning types to know

IBM Journey to AI blog

Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

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A Guide to Unsupervised Machine Learning Models | Types | Applications

Pickl AI

Therefore, it mainly deals with unlabelled data. The ability of unsupervised learning to discover similarities and differences in data makes it ideal for conducting exploratory data analysis. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean.

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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. among supervised models and k-nearest neighbors, DBSCAN, etc., Motivation The motivating question is— ‘What are the chances of survival of a heart failure patient?’.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses.