PyCaret Cheat Sheet

Setup and Installation 🛠️

• Install PyCaret: Use pip install pycaret to get started.
• Dependencies: Ensure all required packages are installed.

Data Preprocessing 📊

• Preprocess data: Utilize PyCaret's preprocessing functions for cleaning data.
• Handle missing values: Automatically impute missing values in your dataset using setup(data, target='target_column').
• Encoding categorical data: Convert categorical columns to numeric using setup(data, target='target_column', categorical_features=['col1', 'col2']).

Classification 📂

• Model setup: Initialize your classification model with setup(data, target='target_column').
• Compare models: Use best_model = compare_models() to find the best classifier.
• Create model: Build a specific model with model = create_model('lr') for logistic regression.
• Tune model: Optimize model hyperparameters using tuned_model = tune_model(model).

Regression 📈

• Model setup: Initialize regression with setup(data, target='target_column').
• Compare models: Evaluate different regressors using best_model = compare_models().
• Create model: Build a regression model with model = create_model('lr') for linear regression.
• Tune model: Optimize model parameters with tuned_model = tune_model(model).

Time Series ⏳

• Setup: Start with setup(data, target='target_column', fold=3, session_id=123) for time series analysis.
• Create model: Use model = create_model('ets') to create an exponential smoothing model.
• Forecasting: Generate forecasts with predict_model(model, fh=10).

Clustering 🗃️

• Model setup: Prepare data for clustering with setup(data).
• Create model: Build a clustering model with model = create_model('kmeans').
• Evaluate clusters: Analyze cluster quality with evaluate_model(model).

Anomaly Detection 🚨

• Setup: Initialize anomaly detection with setup(data).
• Detect anomalies: Identify outliers using model = create_model('iforest').
• Plot anomalies: Visualize anomalies with plot_model(model, plot='tsne').

Model Training 🤖

• Train model: Fit your model with model = create_model('lr').
• Tune model: Improve performance with tuned_model = tune_model(model).
• Ensemble model: Combine models using ensemble_model = ensemble_model(model).

Model Evaluation 📉

• Evaluate model: Use evaluate_model(model) to assess performance.
• Interpret results: Understand model outcomes with interpret_model(model).
• Plot model: Visualize model performance with plot_model(model, plot='auc').

Prediction and Deployment 🚀

• Make predictions: Generate predictions with predictions = predict_model(model, data=new_data).
• Deploy model: Deploy your model using deploy_model(model, model_name='my_model').
• Save model: Save the trained model with save_model(model, 'model_name').
• Load model: Load a saved model with loaded_model = load_model('model_name').

Advanced Features 🌟

• Pipeline creation: Create pipelines for complex workflows using from pycaret.datasets import get_data and from pycaret.classification import *.
• Custom metrics: Define and use custom metrics for evaluation with add_metric('metric_name', 'Metric Display Name', custom_function).
• Automated ML: Leverage AutoML with PyCaret by using best_model = automl().