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
| Name | Plot |
| 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)
- AUC
- Confusion Matrix
- Threshold
- Precision Recall
- Error Plot
- Class Report
- Feature Selection
- Learning Curve
- Manifold Learning
- Calibration Curve
- Validation Curve
- Dimensions
- Feature Importance
- Decision Boundary
Regression
| Name | Plot |
| 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)
- Residuals
- Error Plot
- Cooks Distance
- Feature Selection
- Learning Curve
- Validation Curve
- Manifold
- Feature Importance
Clustering
| Name | Plot |
| 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)
Anomaly Detection
| Name | Plot |
| 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)
Natural Language Processing
| Name | Plot |
| 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)
- Frequency Plot
- Distribution Plot
- Bigram
- Trigram
- Sentiments
- Part of Speech
- t-SNE
- uMAP
- Wordcloud
- Topic Distribution
- Topic Model
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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