Predicting using a classification model
Now that you have fit your classifier, let's use it to predict the type of flower (or class) for some newly-collected flowers.
Information about petal width and length for several new flowers is stored
in the variable targets
. Using the classifier you fit, you'll predict the type of each flower.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Predict the flower type using the array
X_predict
. - Run the given code to visualize the predictions.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create input array
X_predict = targets[['petal length (cm)', 'petal width (cm)']]
# Predict with the model
predictions = ____
print(predictions)
# Visualize predictions and actual values
plt.scatter(X_predict['petal length (cm)'], X_predict['petal width (cm)'],
c=predictions, cmap=plt.cm.coolwarm)
plt.title("Predicted class values")
plt.show()