What is tune model function in NLP in pycaret

Recipe Objective - What is the tune_model function in NLP in pycaret?

PyCaret provides the tune_model function in the NLP module.

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Tune Model function with Example:-

PyCaret prvides "pycaret.anomaly.tune_model()" funtion. Tune model function tunes the "num_topics" parameter of a given model.

Available Models:-

‘lda’ - Latent Dirichlet Allocation

‘lsi’ - Latent Semantic Indexing

‘hdp’ - Hierarchical Dirichlet Process

‘rp’ - Random Projections

‘nmf’ - Non-Negative Matrix Factorization

from pycaret.datasets import get_data
kiva = get_data('kiva')
# importing NLP module
from pycaret.nlp import *
# initialize the setup
setup_name = setup(data = kiva, target = 'en')
# tune model function
tuned_lda = tune_model(model = 'lda', supervised_target = 'status')
tuned_lda


Classification ID:-

‘lr’ - Logistic Regression (Default)

‘knn’ - K Nearest Neighbour

‘nb’ - Naive Bayes

‘dt’ - Decision Tree Classifier

‘svm’ - SVM - Linear Kernel

‘rbfsvm’ - SVM - Radial Kernel

‘gpc’ - Gaussian Process Classifier

‘mlp’ - Multi-Level Perceptron

‘ridge’ - Ridge Classifier

‘rf’ - Random Forest Classifier

‘qda’ - Quadratic Discriminant Analysis

‘ada’ - Ada Boost Classifier

‘gbc’ - Gradient Boosting Classifier

‘lda’ - Linear Discriminant Analysis

‘et’ - Extra Trees Classifier

‘xgboost’ - Extreme Gradient Boosting

‘lightgbm’ - Light Gradient Boosting

‘catboost’ - CatBoost Classifier

Regression ID:-

‘lr’ - Linear Regression (Default)

‘lasso’ - Lasso Regression

‘ridge’ - Ridge Regression

‘en’ - Elastic Net

‘lar’ - Least Angle Regression

‘llar’ - Lasso Least Angle Regression

‘omp’ - Orthogonal Matching Pursuit

‘br’ - Bayesian Ridge