plot and evaluate model function in anomaly detection pycaret
This recipe explains the plot model and evaluate model function in anomaly detection using pycaret. The Plot model function analyzes the performance of a trained model and evaluate model function displays a user interface for analyzing performance of a trained model.
Recipe Objective - What are the plot_model and evaluate_model functions in anomaly detection in pycaret?
PyCaret provides plot_model and evaluate_model function in the anomaly detection module.
Table of Contents
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https://www.projectpro.io/projects/data-science-projects/keras-deep-learning-projects
plot_model function with Example:-
PyCaret provides "pycaret.anomaly.plot_models()" function. The Plot model function analyzes the performance of a trained model.
from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
# Knn model
knn = create_model('knn')
# plotting model
plot_model(knn, plot = 'tsne')
List of plots in PyCaret:-
‘tsne’ - t-SNE (3d) Dimension Plot
‘umap’ - UMAP Dimensionality Plot
evaluate_model function with Example:-
PyCaret provides "pycaret.anomaly.evaluate_model()" function. The evaluate_model function displays a user interface for analyzing the performance of a trained model.
from pycaret.datasets import get_data
anmly = get_data('anomaly')
# importing anomaly module
from pycaret.anomaly import *
# initialize the setup
setup_name = setup(data = anmly)
# Knn model
knn = create_model('knn')
# evaluate_model function
evaluate_model(knn)
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