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()