Using a trained model

Machine learning only becomes useful once a trained model is placed back into a real program. This lesson exists to bridge that gap. We move from “we trained something” to “we can now ask it questions and use the answers,” which is how models earn their place inside larger systems and agents.

Loading a trained model into a program

A trained model is just a Python object that has already learned from data. To use it later, we load it into memory at program startup, just like any other resource the program depends on.

from joblib import load

model = load("planet_classifier.joblib")

At this point, the model is ready to be used. We are not training anything here. We are simply restoring a model so it can perform inference.

Supplying input data for inference

Models expect input data in the same shape and structure they were trained on. For scikit-learn models, this is usually a list of feature values wrapped in another list.

features = [[5.2, 1.3, 0.02]]

This single row represents one item to classify or predict. The exact meaning of each value depends on how the model was trained.

Receiving and interpreting prediction results

Predictions are obtained by calling the model like a tool. The result is returned as data, not text.

prediction = model.predict(features)

The returned value is typically an array. For a single input row, the first element contains the prediction result.

label = prediction[0]

That label might represent a category, a numeric estimate, or a decision boundary, depending on the model.

Using model predictions to influence program behavior

Predictions become useful when they affect what the program does next. A model might choose which path to follow, which content to generate, or which action to take.

if label == "gas_giant":
    page_type = "large_planet.html"
else:
    page_type = "rocky_planet.html"

Here, the model’s output directly shapes program behavior. The model does not act on its own. The surrounding code remains in control.

Treating a model as a callable computational tool

Once loaded, a trained model behaves like a specialized function. It takes structured input and produces structured output, without needing to know anything about the rest of the program.

def classify_planet(features):
    return model.predict([features])[0]

Seen this way, models fit naturally alongside other tools in a system. They compute answers, and the program decides how to use them.

Conclusion

We now know how a trained model re-enters a program and starts doing useful work. It can be loaded, given input, asked for predictions, and treated as a callable component inside larger logic. With this mental model in place, trained models stop feeling mysterious and start behaving like practical computational tools.