How to plot scikit learn classification report?

Question

Is it possible to plot with matplotlib scikit-learn classification report?. Let\'s assume I print the classification report like this:

print \'\\n*Classifica         


        

Answer 1

If you just want to plot the classification report as a bar chart in a Jupyter notebook, you can do the following.

# Assuming that classification_report, y_test and predictions are in scope...
import pandas as pd

# Build a DataFrame from the classification_report output_dict.
report_data = []
for label, metrics in classification_report(y_test, predictions, output_dict=True).items():
    metrics['label'] = label
    report_data.append(metrics)

report_df = pd.DataFrame(
    report_data, 
    columns=['label', 'precision', 'recall', 'f1-score', 'support']
)

# Plot as a bar chart.
report_df.plot(y=['precision', 'recall', 'f1-score'], x='label', kind='bar')

One issue with this visualisation is that imbalanced classes are not obvious, but are important in interpreting the results. One way to represent this is to add a version of the label that includes the number of samples (i.e. the support):

# Add a column to the DataFrame.
report_df['labelsupport'] = [f'{label} (n={support})' 
                             for label, support in zip(report_df.label, report_df.support)]

# Plot the chart the same way, but use `labelsupport` as the x-axis.
report_df.plot(y=['precision', 'recall', 'f1-score'], x='labelsupport', kind='bar')