Categorii: Tot - evaluation - comparison - model - parameters

realizată de Onur Sürhan 3 ani în urmă

165

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The text revolves around the process of selecting and evaluating machine learning algorithms, particularly for clustering tasks. It highlights various classes and functions involved in initializing models, adjusting parameters, and comparing different algorithms to find the best fit.

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changeParams_GMM

parametricGMM Class

changeParams_Affinity

parametricAffinity Class

changeParams_Meanshift

parametricMeanshift Class

changeParams_Spectral

parametricSpectral Class

parametricBirch Class

changeParams_Optics

parametricOptics Class

changeParams_Hier

parametricHier Class

select_algorithm

Algorithm Class

predict

fit

set_params

parameter_selector

Model Class

CalinskiScore

DaviesScore

SilhouetteScore

Score

EvaluationMetrics Class

changeParams_DB

parametricDB Class

init

elbowPlot

specifyParameter

getParametricResults

changeParams_Kmeans

parametricKmeans Class

Compare Algorithms Class

showLabels

getLabels

CompareOtherEvaluationScores

decideTheBest

constructingClasses

Read CSV/Excel

Algorithm selection

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