scale
parameter.png
file using the save
parameter.plot_kwargs
parameter.use_train_data=True
in the plot_model
function.evaluate_model
displays a user interface for analyzing the performance of a trained model. It calls the plot_model function internally.png
file using the save
parameter.save=True
no plot is displayed in the Notebook. plot
parameter.feature
parameter.feature
parameter.reason
plot without passing the specific index of test data, you will get the interactive plot displayed with the ability to select the x and y-axis. This will only be possible if you are using Jupyter Notebook or an equivalent environment. If you want to see this plot for a specific observation, you will have to pass the index in the observation
parameter.observation = 1
means index 1 from the test set.use_train_data
parameter.dashboard
function generates the interactive dashboard for a trained model. The dashboard is implemented using ExplainerDashboard (explainerdashboard.readthedocs.io)deepchecks
require scikit-learn>1.0
version, whereas pycaret requires scikit-learn==0.23.2. Hence after installing deepchecks you must uninstall scikit-learn and reinstall scikit-learn==0.23.2 otherwise you will get an error with pycaret. The future version of pycaret will be scikit-learn>1.0 compatible. pip install autoviz
to use this function.display_format = 'svg'
.check_fairness
function follows the approach known as group fairness, which asks: which groups of individuals are at risk for experiencing harm. check_fairness
provides fairness-related metrics between different groups (also called sub-population).