When using pycaret evaluate_model, plots are not interactive: A Comprehensive Guide to Unleashing Interactive Plots

Are you frustrated with the static plots generated by pycaret’s evaluate_model function? Do you want to learn how to unleash the full potential of interactive plots and take your data analysis to the next level? Look no further! In this article, we’ll dive deep into the world of pycaret and explore the secrets behind creating interactive plots that will revolutionize your data visualization experience.

What is pycaret and evaluate_model?

Pycaret is an open-source, low-code machine learning library in Python that allows data scientists to build and deploy models quickly and efficiently. One of the most popular features of pycaret is the evaluate_model function, which generates a comprehensive report of a model’s performance, including various plots and metrics. However, by default, these plots are static, which can limit their usefulness.

The Problem: Static Plots

Static plots can be frustrating, especially when you want to explore your data in more detail. With static plots, you’re limited to a fixed view, and you can’t interact with the data to gain deeper insights. This is where interactive plots come in – they allow you to hover over data points, zoom in and out, and even animate the data to reveal hidden patterns.

Solution 1: Using the Notebook Environment

One way to enable interactive plots in pycaret is to use a notebook environment like Jupyter Notebook or Google Colab. When you run the evaluate_model function in a notebook, the plots are rendered as interactive HTML objects, allowing you to interact with them directly. This is because notebooks use a web-based interface to display the plots.

import pycaret
from pycaret.regression import *

# Load the dataset
data = pd.read_csv('your_data.csv')

# Create a regression experiment
exp = setup(data, target='target_column')

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

# Evaluate the model
evaluate_model(model)

In a notebook environment, the evaluate_model function will generate interactive plots by default. You can hover over the plots, zoom in and out, and even use the built-in tools to explore the data in more detail.

Solution 2: Using the plotly Library

Another way to enable interactive plots in pycaret is to use the plotly library. Plotly is a popular Python library for creating interactive, web-based visualizations. By using plotly in conjunction with pycaret, you can create interactive plots that can be shared and explored by others.

import pycaret
from pycaret.regression import *
import plotly.graph_objects as go

# Load the dataset
data = pd.read_csv('your_data.csv')

# Create a regression experiment
exp = setup(data, target='target_column')

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

# Evaluate the model using plotly
evaluate_model(model, plot=True, plot_kwargs={'plotly': True})

In this example, we’ve added the `plotly=True` argument to the evaluate_model function. This tells pycaret to use plotly to generate the plots, which will be interactive and web-based.

Solution 3: Using the bokeh Library

A third way to enable interactive plots in pycaret is to use the bokeh library. Bokeh is another popular Python library for creating interactive, web-based visualizations. Like plotly, bokeh allows you to create interactive plots that can be shared and explored by others.

import pycaret
from pycaret.regression import *
import bokeh.plotting as bp

# Load the dataset
data = pd.read_csv('your_data.csv')

# Create a regression experiment
exp = setup(data, target='target_column')

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

# Evaluate the model using bokeh
evaluate_model(model, plot=True, plot_kwargs={'bokeh': True})

In this example, we’ve added the `bokeh=True` argument to the evaluate_model function. This tells pycaret to use bokeh to generate the plots, which will be interactive and web-based.

Tips and Tricks

Here are some additional tips and tricks for working with interactive plots in pycaret:

• Use the `plotly` or `bokeh` library to create custom interactive plots that meet your specific needs.
• Use the built-in tools in plotly or bokeh to add interactive features, such as hover-over text, zooming, and animation.
• Share your interactive plots with others by exporting them as HTML files or sharing them through a web-based platform.

Conclusion

In conclusion, pycaret’s evaluate_model function can generate static plots by default, but with a few simple tricks, you can unleash the full potential of interactive plots. By using a notebook environment, plotly, or bokeh, you can create interactive plots that will revolutionize your data visualization experience. Remember to experiment with different plot types, libraries, and features to find the perfect combination for your specific needs. Happy plotting!

Library	Description
plotly	A popular Python library for creating interactive, web-based visualizations.
bokeh	A Python library for creating interactive, web-based visualizations.
pycaret	An open-source, low-code machine learning library in Python.

Don’t forget to explore the official documentation for pycaret, plotly, and bokeh to learn more about their features and capabilities.

Frequently Asked Questions

Q: Why are the plots generated by pycaret’s evaluate_model function static by default?

A: The plots are static by default because pycaret uses matplotlib, a popular Python library for creating static plots, to generate the plots.

Q: Can I use other libraries besides plotly and bokeh to create interactive plots?

A: Yes, you can use other libraries like altair, seaborn, or holoviews to create interactive plots.

Q: How do I share my interactive plots with others?

A: You can share your interactive plots by exporting them as HTML files or sharing them through a web-based platform like a dashboard or a blog post.

Q: Are interactive plots only useful for data visualization?

A: No, interactive plots can be used for a wide range of applications, including data exploration, presentation, and storytelling.

Q: Can I use pycaret’s evaluate_model function with other types of models besides regression?

A: Yes, pycaret’s evaluate_model function can be used with other types of models, including classification, clustering, and anomaly detection models.

Frequently Asked Question

Having trouble with pycaret’s evaluate_model function? Wondering why the plots aren’t interactive?