Understand the Python Code

The Complete Code

First, look at the complete example before breaking it into individual parts.

from sklearn.tree import DecisionTreeClassifier

# Training data
X = [
    [1, 55],
    [2, 60],
    [3, 70],
    [5, 85],
    [6, 90],
    [7, 95]
]

# Labels
y = [
    "Fail",
    "Fail",
    "Fail",
    "Pass",
    "Pass",
    "Pass"
]

# Create the model
model = DecisionTreeClassifier(random_state=42)

# Train the model
model.fit(X, y)

# Make a prediction
prediction = model.predict([[5, 88]])

print(prediction)

Import the Decision Tree

The first line is:

from sklearn.tree import DecisionTreeClassifier

This imports the DecisionTreeClassifier class from scikit-learn.

We need this class because it provides the Decision Tree algorithm we want to use.

What Is X?

Next we create the input data:

X = [
    [1, 55],
    [2, 60],
    [3, 70],
    [5, 85],
    [6, 90],
    [7, 95]
]

X contains the features the model will use to make its decisions.

Look at the first row:

[1, 55]

This means:

What Is y?

Next we create the labels:

y = [
    "Fail",
    "Fail",
    "Fail",
    "Pass",
    "Pass",
    "Pass"
]

y contains the correct answer for each row in X.

Create the Model

Now we create the Decision Tree:

model = DecisionTreeClassifier(random_state=42)

This creates a Decision Tree classifier and stores it inside the variable called model.

Think of this as getting an empty decision-making tool ready for training.

What Does random_state=42 Mean?

You may notice this:

random_state=42

Some Machine Learning algorithms can involve randomness during their operation.

Setting random_state gives that randomness a fixed starting point, which helps make results reproducible when randomness is involved.

Train the Model With fit()

This is one of the most important lines:

model.fit(X, y)

The fit() method trains the Decision Tree using our examples.

During training, the tree looks for useful splits that help separate the classes.

What Does the Tree Learn?

The model can discover useful decision rules from the training data.

For our simple dataset, one useful pattern could be:

We do not manually write the decision rule in our Python code.

The algorithm finds the splits from the training data.

Make a Prediction With predict()

After training, we can give the model a new student:

prediction = model.predict([[5, 88]])

The new student has:

The trained tree uses the decision rules it learned during training to determine the predicted class.

What Does predict() Return?

If we print the prediction:

print(prediction)

We may see:

['Pass']

Notice that the result is inside a list. That is because predict() is designed to make predictions for one or more input rows.

One Line, One Job

The easiest way to remember the code is to understand the job of each important part.

The Whole Code in Plain English

We can translate the Python code into simple English:

Two Different Things: Training vs Prediction

A common beginner mistake is thinking that fit() and predict() do the same thing.

Check Your Understanding