In this example we use VerbaGPT to classify the type of Iris flower from the famous iris dataset1.
Video:
User question: Give me a model to classify the iris flower. Give me the confusion matrix as a performance metric.
VerbaGPT response: 
Cost of query: $0.00*
* Using open-source llama4-maverick model from OpenRouter
[[50 0 0] [ 0 47 3] [ 0 1 49]]
VerbaGPT answers the question correctly and gives us exactly what we asked for. The diagonal represents correctly classified examples, and the off-diagonal items in the confusion matrix are the errors.
Check out the full VerbaGPT read-out by click on below.
VerbaGPT Message Window
Here is a code snippet that you can use to classify the iris flower using a logistic regression model and generate a confusion matrix as a performance metric.
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
# Load iris data from the database
query = "SELECT * FROM iris"
iris_data = pd.read_sql(query, conn)
# Split data into features and target
X = iris_data.drop('target', axis=1)
y = iris_data['target']
# Create and fit logistic regression model
model = LogisticRegression()
model.fit(X, y)
# Generate predictions and confusion matrix
y_pred = model.predict(X)
confusion = confusion_matrix(y, y_pred)
# Print confusion matrix
print(confusion)
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