Plot Model

Analyzing performance of trained machine learning model is an integral step in any machine learning workflow. Analyzing model performance in PyCaret is as simple as writing plot_model. The function takes trained model object and type of plot as string within plot_model function.

Plots by Module

Classification
Regression
Clustering
Anomaly Detection
Natural Language Processing
Association Rule Mining

Classification

Plot	Abbrev. String
Area Under the Curve	‘auc’
Discrimination Threshold	‘threshold’
Precision Recall Curve	‘pr’
Confusion Matrix	‘confusion_matrix’
Class Prediction Error	‘error’
Classification Report	‘class_report’
Decision Boundary	‘boundary’
Recursive Feature Selection	‘rfe’
Learning Curve	‘learning’
Manifold Learning	‘manifold’
Calibration Curve	‘calibration’
Validation Curve	‘vc’
Dimension Learning	‘dimension’
Feature Importance	‘feature’
Model Hyperparameter	‘parameter’

Code

# Importing dataset
from pycaret.datasets import get_data
diabetes = get_data('diabetes')

# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = diabetes, target = 'Class variable')

# creating a model
lr = create_model('lr')

# plotting a model
plot_model(lr)

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Regression

Plot	Abbrev. String
Residuals Plot	‘residuals’
Prediction Error Plot	‘error’
Cooks Distance Plot	‘cooks’
Recursive Feature Selection	‘rfe’
Learning Curve	‘learning’
Validation Curve	‘vc’
Manifold Learning	‘manifold’
Feature Importance	‘feature’
Model Hyperparameter	‘parameter’

Code

# Importing dataset
from pycaret.datasets import get_data
boston = get_data('boston')

# Importing module and initializing setup
from pycaret.regression import *
reg1 = setup(data = boston, target = 'medv')

# creating a model
lr = create_model('lr')

# plotting a model
plot_model(lr)

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Clustering

Plot	Abbrev. String
Cluster PCA Plot (2d)	‘cluster’
Cluster TSnE (3d)	‘tsne’
Elbow Plot	‘elbow’
Silhouette Plot	‘silhouette’
Distance Plot	‘distance’
Distribution Plot	‘distribution’

Code

# Importing dataset
from pycaret.datasets import get_data
jewellery = get_data('jewellery')

# Importing module and initializing setup
from pycaret.clustering import *
clu1 = setup(data = jewellery)

# creating a model
kmeans = create_model('kmeans')

# plotting a model
plot_model(kmeans)

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Anomaly Detection

Plot	Abbrev. String
t-SNE (3d) Dimension Plot	‘tsne’
UMAP Dimensionality Plot	‘umap’

Code

# Importing dataset
from pycaret.datasets import get_data
anomalies = get_data('anomaly')

# Importing module and initializing setup
from pycaret.anomaly import *
ano1 = setup(data = anomalies)

# creating a model
iforest = create_model('iforest')

# plotting a model
plot_model(iforest)

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Natural Language Processing

Plot	Abbrev. String
Word Token Frequency	‘frequency’
Word Distribution Plot	‘distribution’
Bigram Frequency Plot	‘bigram’
Trigram Frequency Plot	‘trigram’
Sentiment Polarity Plot	‘sentiment’
Part of Speech Frequency	‘pos’
t-SNE (3d) Dimension Plot	‘tsne’
Topic Model (pyLDAvis)	‘topic_model’
Topic Infer Distribution	‘topic_distribution’
Word cloud	‘wordcloud’
UMAP Dimensionality Plot	‘umap’

Code

# Importing dataset
from pycaret.datasets import get_data
kiva = get_data('kiva')

# Importing module and initializing setup
from pycaret.nlp import *
nlp1 = setup(data = kiva, target = 'en')

# creating a model
lda = create_model('lda')

# plotting a model
plot_model(lda)

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Association Rule Mining

Plot	Abbrev. String
Support, Confidence and Lift (2d)	‘frequency’
Support, Confidence and Lift (3d)	‘distribution’

Code

# Importing dataset
from pycaret.datasets import get_data
france = get_data('france')

# Importing module and initializing setup
from pycaret.arules import *
arul1 = setup(data = france, transaction_id = 'Invoice', item_id = 'Description')

# creating a model
model = create_model(metric = 'confidence')

# plotting a model
plot_model(model)

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Try this next

Interpret Model

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