Why Can’t We Write DecisionTreeRegressor.fit() Directly?

[Rajeev Bagra]

When learning Scikit-learn, beginners often see code like:

from sklearn.tree import DecisionTreeRegressor

iowa_model = DecisionTreeRegressor(random_state=1)
iowa_model.fit(train_X, train_y)

and wonder:

Why do we need iowa_model?
Can’t we simply write:

DecisionTreeRegressor.fit(train_X, train_y)

The short answer is: No.

Let’s understand why.

---

Classes vs Objects

In Python, DecisionTreeRegressor is a class.

A class is a blueprint used to create objects.

Think of it like this:

House Blueprint
        ↓
Actual House

Similarly:

DecisionTreeRegressor
        ↓
Model Object

The class itself is not a trained model.

---

Step 1: Create a Model Object

Before training, an object must be created.

from sklearn.tree import DecisionTreeRegressor

model = DecisionTreeRegressor(random_state=1)

Now Python has an actual model object stored in the variable:

model

---

Step 2: Train the Model

After creating the object, we can call:

model.fit(train_X, train_y)

The fit() method trains that specific model.

---

Why Doesn’t This Work?

Suppose we write:

DecisionTreeRegressor.fit(train_X, train_y)

Python sees:

Class.fit(...)

but there is no actual model instance available.

Python does not know:

• Which model should be trained
• Where the learned information should be stored
• Which tree object should receive the data

Therefore an error occurs.

---

What Kaggle Is Doing

Kaggle often uses:

iowa_model = DecisionTreeRegressor(random_state=1)
iowa_model.fit(train_X, train_y)

The name iowa_model is simply a variable name.

Because the dataset contains Iowa housing data, the author chose a descriptive name.

Any valid variable name would work.

For example:

model = DecisionTreeRegressor(random_state=1)
model.fit(train_X, train_y)

or

tree = DecisionTreeRegressor(random_state=1)
tree.fit(train_X, train_y)

---

Can We Create and Train in One Line?

Yes.

DecisionTreeRegressor(random_state=1).fit(train_X, train_y)

This works because:

Create Object
        ↓
Train Object

all happens in a single statement.

---

Why Is This Usually Avoided?

Consider:

DecisionTreeRegressor(random_state=1).fit(train_X, train_y)

After training finishes, the model is not stored anywhere.

Later you cannot easily write:

predict(...)

because you no longer have a reference to the trained model.

Instead developers usually write:

model = DecisionTreeRegressor(random_state=1)
model.fit(train_X, train_y)

predictions = model.predict(val_X)

Now the trained model can be reused whenever needed.

---

A Real-World Analogy

Imagine a factory blueprint.

Blueprint
    ↓
Machine
    ↓
Training
    ↓
Usage

You cannot train a blueprint.

You first build a machine from the blueprint and then train the machine.

Similarly:

DecisionTreeRegressor
          ↓
        model
          ↓
      model.fit()
          ↓
    model.predict()

---

Complete Flow

DecisionTreeRegressor
          │
          ▼
Create Model Object
          │
          ▼
model = DecisionTreeRegressor()
          │
          ▼
model.fit(train_X, train_y)
          │
          ▼
Trained Model
          │
          ▼
model.predict(val_X)
          │
          ▼
Predictions

---

Key Takeaways

✅ DecisionTreeRegressor is a class, not a trained model.

✅ A model object must be created before calling fit().

✅ iowa_model is simply a variable name chosen by Kaggle.

✅ Any variable name such as model or tree would work.

✅ DecisionTreeRegressor.fit(...) fails because no model instance exists.

✅ The most common pattern is:

model = DecisionTreeRegressor(random_state=1)
model.fit(train_X, train_y)

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